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Cumming, Jonathan. "Clinical decision support." Thesis, Durham University, 2006. http://etheses.dur.ac.uk/1814/.
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Muller, Johann Heinrich. "A clinical engineering decision support system." Master's thesis, University of Cape Town, 1988. http://hdl.handle.net/11427/26533.
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The use of technology in health-care today is increasing dramatically with a corresponding increase in cost and complexity to provide and support it. The degree to which a hospital manages this technology affects its ability to treat patients, to perform research, to teach and to attract competent staff. This thesis project has identified the role that clinical engineering could play in health-care technology provision and support in South Africa. A system synthesis technique was employed to develop an idealized clinical engineering model (ICE) that would satisfy South African technological requirements. An extensive literature survey of the current status of clinical engineering in both developed and developing countries was undertaken to provide input to the synthesis process. Surveys were then conducted to determine the actual current status of clinical engineering and its environment in the RSA. To enable such an idealised department to function as defined, it must be supported by appropriate and timeous information. The information needs of the idealised clinical engineering model were analysed and a corresponding decision support system (DSS) defined. Further surveys were conducted to test the applicability and acceptability of the idealised clinical engineering model. The feasibility of implementing the idealised clinical engineering model in South Africa was investigated and recommendations were made based on the research results of this thesis to bring the actual status of clinical engineering closer to the idealised model. ii
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Patrick, Louise. "Expressed support, perceived support and physical ability in chronic pain patients." Thesis, University of Ottawa (Canada), 1992. http://hdl.handle.net/10393/7783.
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This study investigated the relationship between social support within the marital context, and physical ability in chronic pain patients. Fifty patients diagnosed with chronic pain and their spouses participated in the study. Each patient was asked to exercise on a stationary bicycle, with his/her spouse present. The interactions between patient and spouse were videorecorded and the spouse's behaviour was rated for the amount of task-related and emotional support expressed. The relationships among the spouse's behaviour, the patient's perception of that behaviour and the patient's physical ability were examined. Marital adjustment, depressive symptomatology and the spouse's perception of the patient's physical limitations were investigated as predictors of expressed and perceived support. Zero-order correlations replicated the previously demonstrated positive relationships among the patient's report of spouse support, pain intensity and marital adjustment. Using hierarchical regression to control for the patient's depressive symptomatology and marital adjustment, it was found that observed spouse support was positively related to the patient's physical ability, accounting for 13% of the variance. When pain severity was also entered into the equation, results indicated that pain intensity was the only significant predictor and was negatively related to the patient's physical ability, accounting for 43% of the variance. No significant predictor of the spouse's expressed support was identified, while the patient's marital adjustment was positively related to his/her perception of support. Descriptive reports by patients of their perception of support during the physical ability task indicated that patients experienced task and emotional support differently. The majority of patients reported that emotional support was experienced as supportive and helpful, but task-related support was not.
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Denney, Kimberly B. "Assessing Clinical Software User Needs for Improved Clinical Decision Support Tools." ScholarWorks, 2015. https://scholarworks.waldenu.edu/dissertations/1563.
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Consolidating patient and clinical data to support better-informed clinical decisions remains a primary function of electronic health records (EHRs). In the United States, nearly 6 million patients receive care from an accountable care organization (ACO). Knowledge of clinical decision support (CDS) tool design for use by physicians participating in ACOs remains limited. The purpose of this quantitative study was to examine whether a significant correlation exists between characteristics of alert content and alert timing (the independent variables) and physician perceptions of improved ACO quality measure adherence during electronic ordering (the dependent variable). Sociotechnical theory supported the theoretical framework for this research. Sixty-nine physician executives using either a Cerner Incorporated or Epic Systems EHR in a hospital or health system affiliated ACO participated in the online survey. The results of the regression analysis were statistically significant, R2 = .108, F(2,66) = 3.99, p = .023, indicating that characteristics of alert content and timing affect physician perceptions for improving their adherence to ACO quality measures. However, analysis of each independent variable showed alert content highly correlated with the dependent variable (p = .007) with no significant correlation found between workflow timing and the dependent variable (p = .724). Understanding the factors that support physician acceptance of alerts is essential to third-party software developers and health care organizations designing CDS tools. Providing physicians with improved EHR-integrated CDS tools supports the population health goal of ACOs in delivering better patient care.
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Sánchez, Pinsach David. "Handling Missing Data in Clinical Decision Support." Doctoral thesis, Universitat Autònoma de Barcelona, 2020. http://hdl.handle.net/10803/671318.
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Decidir quins són els millors tractaments és una tasca complexa quan els pacients pateixen múltiples problemes i quan un equip multidisciplinari està involucrat en la intervenció. Sempre hi ha més d’una opció de tractament i els resultats a vegades es poden veure en un període curt o un cop finalitzat el tractament. En aquest context, el disseny de sistemes eficaços de suport a la decisió clínica (CDSS) per ajudar als metges a seleccionar les intervencions més apropiades segueix sent avui dia un desafiament.
La quantitat de dades disponibles no sempre és la mateixa per a tots els pacients, especialment en les fases primerenques del tractament, dificultant la inferència en els CDSS. Per millorar les capacitats dels CDSS, es proposen diferents components per als tractaments de llarga durada. Un primer component se centra a millorar la qualitat de les inferències en les dades desconegudes. L’algoritme d’imputació múltiple dinàmica (DMI) es presenta com una metodologia eficaç per a la millora de les dades. DMI és capaç d’adaptar-se a diferents escenaris amb un percentatge alt o baix de dades desconegudes. Els experiments realitzats revelen que DMI és especialment competitiu en problemes de regressió. Un segon component està dedicat a compensar les mesures de confiança, donada la incertesa associada a la informació desconeguda, incorporant mesures d’Informació Mútua en les confiances existents. El tercer component, basat en un algorisme de detecció de comunitats està orientat a trobar relacions entre decisions clíniques que no són explícites. Finalment, per il·lustrar l’aplicabilitat dels diferents components proposats, es presenten dos casos d’ús clínics reals. Un en el context hospitalari i un altre en el context del domicili.Decidir cuáles son los mejores tratamientos es una tarea compleja cuando los pacientes sufren múltiples problemas y cuando un equipo multidisciplinario está involucrado en la intervención. Siempre hay más de una opción de tratamiento y los resultados a veces se pueden ver en un período corto o al final, una vez finalizado el tratamiento.En este contexto, el diseño de sistemas eficaces de soporte a la decisión clínica (CDSS) para ayudar a los médicos a seleccionar las intervenciones más apropiadas sigue siendo hoy en día un desafío. La cantidad de datos disponibles no siempre es la misma para todos los pacientes, especialmente en las fases tempranas del tratamiento, lo que dificulta la inferencia en los CDSS. Para mejorar las capacidades de los CDSS, se proponen diferentes componentes para tratamientos a largo plazo. Un primer componente se centra en mejorar la calidad de las inferencias en los datos desconocidos. El algoritmo de imputación múltiple dinámica (DMI) se presenta como un metodología eficaz para la mejora de los datos. DMI es capaz de adaptarse a diferentes escenarios con un porcentaje alto o bajo de datos desconocidos. Los experimentos realizados revelan que DMI es especialmente competitivo en problemas de regresión. Un segundo componente está dedicado a compensar las medidas de confianza, dada la incertidumbre asociada a la información desconocida, incorporando medidas de Información Mutua en las confianzas existentes. El tercer componente basado en un algoritmo de detección de comunidades esta orientado a encontrar relaciones entre decisiones clínicas que no son explícitas. Finalmente, para ilustrar la aplicabilidad de los diferentes componentes propuestos, se presentan dos casos de uso clínico reales. Uno en el contexto hospitalario y otro en el contexto del domicilio.
Deciding which are the best treatments is a complex task when patients suffer multiple impairments and when a multidisciplinary team is involved in the intervention. There is always more than a unique treatment option and the results sometimes can be viewed in a short period or only be capable to be measured when the treatment is finished. In this context, the design of effective Clinical Decision Support Systems (CDSS) to help clinicians to select most appropriate interventions is still a challenge. The amount of available data is not always the same for all patients, especially in early treatment stages, hindering the inference in CDSS. To improve the capabilities of CDSS, different components are proposed within a CDSS framework for long-term treatments. A first component is focused on improving the quality of the inferences in missing data scenarios. The Dynamic Multiple Imputation (DMI) algorithm is presented as an effective methodology for data enhancement in CDSS. DMI is capable to adapt to different scenarios with a low or high percentage of missing data. Several experiments conducted reveal that DMI is competitive with regression problems. A second component is devoted to weigh confidence measures, given the uncertainty associated to missing information, by incorporating Mutual Information measures in confidence existing estimators. A third component, based on a community detection algorithm, is proposed to find relationships between clinical decisions that are not explicit. Finally, to illustrate the applicability of different proposed components, two real clinical use cases with chronic patients are presented. The first in the hospital context and the other in the home context.
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Merode, Godefridus Gerardus van. "Decision support for clinical laboratory capacity planning." [Maastricht : Maastricht : Rijksuniversiteit Limburg] ; University Library, Maastricht University [Host], 1994. http://arno.unimaas.nl/show.cgi?fid=6591.
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Zhuang, Wenjie. "Query Expansion Study for Clinical Decision Support." Thesis, Virginia Tech, 2018. http://hdl.handle.net/10919/82068.
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Information retrieval is widely used for retrieving relevant information among a variety of data, such as text documents, images, audio and videos. Since the first medical batch retrieval system was developed in mid 1960s, significant research efforts have focused on applying information retrieval to medical data. However, despite the vast developments in medical information retrieval and accompanying technologies, the actual promise of this area remains unfulfilled due to properties of medical data and the huge volume of medical literature.
Specifically, the recall and precision of the selected dataset from the TREC clinical decision support track are low. The overriding objective of this thesis is to improve the performance of information retrieval techniques applied to biomedical text documents. We have focused on improving recall and precision among the top retrieved results. To that end, we have removed redundant words, and then expanded queries by adding MeSH terms in TREC CDS topics. We have also used other external data sources and domain knowledge to implement the expansion. In addition, we have also considered using the doc2vec model to optimize retrieval. Finally, we have applied learning to rank which sorts documents based on relevance and put relevant documents in front of irrelevant documents, so as to return the relevant retrieved data on the top. We have discovered that queries, expanded with external data sources and domain knowledge, perform better than applying the TREC topic information directly.Master of Science
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Brandt, Joachim. "Clinical decision support in breast cancer using neurocomputing modelling of clinical trial data." Thesis, Coventry University, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.251860.
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Asare, Adam L. "Improving clinical hematopathology quality using decision support methods." Free to MU Campus, others may purchase, 2002. http://wwwlib.umi.com/cr/mo/fullcit?p3052142.
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Yet, Barbaros. "Bayesian networks for evidence based clinical decision support." Thesis, Queen Mary, University of London, 2013. http://qmro.qmul.ac.uk/xmlui/handle/123456789/9096.
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Evidence based medicine (EBM) is defined as the use of best available evidence for decision making, and it has been the predominant paradigm in clinical decision making for the last 20 years. EBM requires evidence from multiple sources to be combined, as published results may not be directly applicable to individual patients. For example, randomised controlled trials (RCT) often exclude patients with comorbidities, so a clinician has to combine the results of the RCT with evidence about comorbidities using his clinical knowledge of how disease, treatment and comorbidities interact with each other. Bayesian networks (BN) are well suited for assisting clinicians making evidence-based decisions as they can combine knowledge, data and other sources of evidence. The graphical structure of BN is suitable for representing knowledge about the mechanisms linking diseases, treatments and comorbidities and the strength of relations in this structure can be learned from data and published results. However, there is still a lack of techniques that systematically use knowledge, data and published results together to build BNs. This thesis advances techniques for using knowledge, data and published results to develop and refine BNs for assisting clinical decision-making. In particular, the thesis presents four novel contributions. First, it proposes a method of combining knowledge and data to build BNs that reason in a way that is consistent with knowledge and data by allowing the BN model to include variables that cannot be measured directly. Second, it proposes techniques to build BNs that provide decision support by combining the evidence from meta-analysis of published studies with clinical knowledge and data. Third, it presents an evidence framework that supplements clinical BNs by representing the description and source of medical evidence supporting each element of a BN. Fourth, it proposes a knowledge engineering method for abstracting a BN structure by showing how each abstraction operation changes knowledge encoded in the structure. These novel techniques are illustrated by a clinical case-study in trauma-care. The aim of the case-study is to provide decision support in treatment of mangled extremities by using clinical expertise, data and published evidence about the subject. The case study is done in collaboration with the trauma unit of the Royal London Hospital.
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Nguyen, Tan-Nhu. "Clinical decision support system for facial mimic rehabilitation." Thesis, Compiègne, 2020. http://www.theses.fr/2020COMP2590.
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La paralysie faciale affecte négativement la vie professionnelle, sociale et personnelle des patients concernés. La récupération des mimiques faciales dans des conditions normales et symétriques permet à ces patients d'améliorer leurs qualités de vie. La rééducation fonctionnelle est une étape clinique importante pour améliorer les qualités des interventions chirurgicales. Cependant, la rééducation faciale reste actuellement un défi scientifique, technologique et clinique majeur. En particulier, l'approche conventionnelle manque des retours quantitatifs et objectifs pour optimiser les gestes et les exercices associées. L'objectif de cette thèse est de développer et d'évaluer un système d'aide à la rééducation fonctionnelle de la mimique faciale. La thèse a six contributions principales : (1) un nouveau processus de génération de la tête patient-spécifique avec la texture à partir d'un capteur sans contact de type Kinect ; (2) un nouveau processus de prédiction du crâne à partir de la surface de la tête en utilisant la modélisation statistique de la forme ; (3) une nouvelle méthode d'évaluation des mouvements de la mimique faciale en se basant sur les propriétés musculaires ; (4) un nouveau système de jeu sérieux pour la rééducation fonctionnelle de la mimique faciale (5) un nouveau système d'aide à la décision clinique pour le visage et (6) un guide de référence pour le développement de systèmes de simulation médicale en considérant la déformation des tissus mous en temps réel. Cette thèse ouvre de nouvelles perspectives liées aux différents domaines de recherche allant de la vision par ordinateur (génération automatique des modèles patient-spécifique à partir d'un capteur visuel), la modélisation biomécanique, et l'ingénierie des systèmes pour la rééducation fonctionnelle de la mimique facialeFacial disorders negatively affect professional, social, and personal lives of involved patients.Thus, recovery of facial mimics into normal and symmetrical conditions allows these patients to improve their life qualities. Functional rehabilitation of facial disorders is an important clinical step to improve qualities of surgical interventions and drug therapies. However, facialmimic rehabilitation currently remains a major scientific, technological, and clinical challenge.Especially, conventional rehabilitation processes lack of quantitative and objective biofeedbacks. Moreover, rehabilitation exercises just included long-term and repetitive actions. This makes patients less ambitious for completing their training programs. Besides, numerous modeling methods, interaction devices, and system architectures have been successfully employed in clinical applications, but they have not been successfully applied for facial mimic rehabilitation. Consequently, this thesis was conducted to complement these drawbacks by designing a clinical decision-support system for facial mimic rehabilitation. Especially, patientspecific models and serious games were integrated with the system for providing quantitative and objective bio-feedbacks and training motivations. The thesis has six main contributions: (1) a novel real-time subject-specific head generation & animation systems, (2) a novel head-to-skull prediction process, (3) a muscle-oriented patientspecific facial paralysis grading system, (4) a novel serious game system for facial mimic rehabilitation, (5) a novel clinical decision-support system for facial mimic rehabilitation, and (6) a reference guide for developing real-time soft-tissues simulation systems. This thesis opens new avenues for new research areas relating to automatic generation of patient specific head from visual sensor and internal structures using statistical shape modeling and real-time modeling and simulation for facial mimic rehabilitation
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Poslad, S. J. "Clinical evaluation of artificial lung performance." Thesis, University of Newcastle Upon Tyne, 1987. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.378853.
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Samadian, Soroush. "Constructing and applying semantic models of clinical phenotypes to support web-embedded clinical research." Thesis, University of British Columbia, 2013. http://hdl.handle.net/2429/44634.
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The adoption of the Semantic Web by life sciences provides exciting opportunities for exploring novel ways to conduct biomedical research. Particularly, the approval of the Web Ontology Language (OWL) by the World Wide Web Consortium (W3C) has provided a global standard for shared representation of biomedical knowledge in the form of ontologies. However, though there are numerous examples of bio-ontologies being used to describe “what is” (i.e., a universal view of reality), there is a dearth of examples where ontologies are used to describe “what might be” (i.e., a hypothetical view of reality).
This thesis proposes that, to achieve scientific rigor, it is important to consider approaches for explicitly representing subjective knowledge in a particular domain - in particular, we examine phenotypic classifications in the clinical domain. We provide supporting evidence, that the OWL is suitable for formally representing these subjective perspectives. Envisioning ontologies as hypothetical, contextual and subjective is a notable departure from the commonly-held view in the life-sciences, where ontologies (ostensibly) represent some "universal truth". We support these arguments with both empirical and quantitative studies. We demonstrate that, when expressed in OWL, many phenotypic classification systems can be accurately modeled in silico. We then demonstrate that these knowledge-models can be "personalized", and show that such models can enable the automated analysis of data in a transparent manner. This results in more rigorous clinical research, while simultaneously allowing the clinicians to maintain their role as the final arbiters of decisions.
Finally, we investigate methodologies that might facilitate the encoding and sharing of personalized expert-knowledge by non-knowledge-engineers - a necessary step in making these ideas useful to the clinical community. The knowledge-acquisition bottleneck is the primary barrier to the widespread use of ontologies in life sciences. Thus, we investigate data-driven methodologies to automatically extract knowledge from existing data-systems, and show that it is possible to "boot-strap" the construction of knowledge models through various data-mining algorithms.
Taken together, these studies begin to reveal a path toward Web-embedded, in silico clinical research, where knowledge is explicit, transparent, personalized, modular, globally-shared, re-used, and dynamically applied to the interpretation and analysis of clinical datasets.
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Lillebo, Børge. "Supporting Clinical Perception : A multi-method approach to how technology may support clinical perception." Doctoral thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for nevromedisin, 2014. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-25159.
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BACKGROUND Patient care and management in hospitals is more difficult than ever before. Not only must clinicians perceive and interpret vast amounts of medical information related to single patient management, they are also expected to coordinate their own and their patients’ activities to achieve most health benefit per value spent. The overall objective of this thesis was to explore, develop and evaluate technology that may support perception and interpretation of information related to individual patient management and care coordination. METHODS The research has been conducted within the context of perioperative care, including operating theatres, post-anaesthesia care units, trauma team management of emergency patients and assessment of individual patients’ laboratory test results. Field work with focused interviews and in-depth interviews with perioperative staff were carried out to understand what information supports perioperative staff members in managing their own and their patients’ activities, and how that information is perceived. Patient status overviews were iteratively developed and evaluated through simulated ward work scenarios to understand what information supports surgical ward nurses in managing perioperative care, and how that information can be presented on a digital whiteboard without compromising patient privacy. An observational retrospective cohort study was conducted to evaluate the importance of projected perception of a care activity on patient management. Finally, we did a balanced, crossover experiment with medical students as participants to explore the effects of four laboratory test result presentation formats on the quality and efficiency of perception and interpretation of such results. RESULTS Perioperative activities unfold unpredictably, and perioperative care management relies heavily on ad hoc communication. Although schedules are perceived as important collaborative artefacts, they provide limited support for timely execution of individual work. A patient management status board including a continuously updated list of recent care events as well as a list of estimated onset of expected future events was welcomed by ward nurses. Such a status board was expected to facilitate perception and improve awareness on new care information as well as reduce the workload of coordinating care. Continuously updated estimated projections of expected future events were considered to be an effective means of interdepartmental communication. There was a significant association between the efficiency of the initial intrahospital trauma patient management and the time from trauma team activation until patient arrival (i.e. the amount of temporal projection). The descriptive data suggested that 10-20 minutes pre-arrival activation of the team was optimal timing. Pre-activation trauma team notification was not associated with the efficiency of patient management. The formats with which laboratory results were presented influenced both speed and quality of the assessment. Participants preferred different presentation formats for different kinds of patients. A table sufficed for sets of laboratory results consisting of few blood samples, but line graph visualisations seemed favourable for sets of laboratory results consisting of many samples. No single presentation format was superior in all respects. CONCLUSION Presenting proper information at the right place and right time is important, and for some information presentation format also may matter. Providing clinicians with timely updates on care activities and estimated onset of expected future events seems to be a fruitful technological solution to support coordination of hospital services. Furthermore, information visualisation has a potential of enhancing the perceptual and cognitive skills of clinicians – influencing the clinical assessment of patient data both qualitatively and quantitatively.
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Ogunsanya, Oluwole Victor. "Decision support using Bayesian networks for clinical decision making." Thesis, Queen Mary, University of London, 2012. http://qmro.qmul.ac.uk/xmlui/handle/123456789/8688.
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This thesis investigates the use of Bayesian Networks (BNs), augmented by the Dynamic Discretization Algorithm, to model a variety of clinical problems. In particular, the thesis demonstrates four novel applications of BN and dynamic discretization to clinical problems. Firstly, it demonstrates the flexibility of the Dynamic Discretization Algorithm in modeling existing medical knowledge using appropriate statistical distributions. Many practical applications of BNs use the relative frequency approach while translating existing medical knowledge to a prior distribution in a BN model. This approach does not capture the full uncertainty surrounding the prior knowledge. Secondly, it demonstrates a novel use of the multinomial BN formulation in learning parameters of categorical variables. The traditional approach requires fixed number of parameters during the learning process but this framework allows an analyst to generate a multinomial BN model based on the number of parameters required. Thirdly, it presents a novel application of the multinomial BN formulation and dynamic discretization to learning causal relations between variables. The idea is to consider competing causal relations between variables as hypotheses and use data to identify the best hypothesis. The result shows that BN models can provide an alternative to the conventional causal learning techniques. The fourth novel application is the use of Hierarchical Bayesian Network (HBN) models, augmented by dynamic discretization technique, to meta-analysis of clinical data. The result shows that BN models can provide an alternative to classical meta analysis techniques. The thesis presents two clinical case studies to demonstrate these novel applications of BN models. The first case study uses data from a multi-disciplinary team at the Royal London hospital to demonstrate the flexibility of the multinomial BN framework in learning parameters of a clinical model. The second case study demonstrates the use of BN and dynamic discretization to solving decision problem. In summary, the combination of the Junction Tree Algorithm and Dynamic Discretization Algorithm provide a unified modeling framework for solving interesting clinical problems.
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Mannamparambil, Chandrasekharan Gopikrishnan. "Ontology driven clinical decision support for early diagnostic recommendations." Thesis, City, University of London, 2018. http://openaccess.city.ac.uk/21167/.
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Diagnostic error is a significant problem in medicine and a major cause of concern for patients and clinicians and is associated with moderate to severe harm to patients. Diagnostic errors are a primary cause of clinical negligence and can result in malpractice claims. Cognitive errors caused by biases such as premature closure and confirmation bias have been identified as major cause of diagnostic error. Researchers have identified several strategies to reduce diagnostic error arising from cognitive factors. This includes considering alternatives, reducing reliance on memory, providing access to clear and well-organized information. Clinical Decision Support Systems (CDSSs) have been shown to reduce diagnostic errors. Clinical guidelines improve consistency of care and can potentially improve healthcare efficiency. They can alert clinicians to diagnostic tests and procedures that have the greatest evidence and provide the greatest benefit. Clinical guidelines can be used to streamline clinical decision making and provide the knowledge base for guideline based CDSSs and clinical alert systems. Clinical guidelines can potentially improve diagnostic decision making by improving information gathering. Argumentation is an emerging area for dealing with unstructured evidence in domains such as healthcare that are characterized by uncertainty. The knowledge needed to support decision making is expressed in the form of arguments. Argumentation has certain advantages over other decision support reasoning methods. This includes the ability to function with incomplete information, the ability to capture domain knowledge in an easy manner, using non-monotonic logic to support defeasible reasoning and providing recommendations in a manner that can be easily explained to clinicians. Argumentation is therefore a suitable method for generating early diagnostic recommendations. Argumentation-based CDSSs have been developed in a wide variety of clinical domains. However, the impact of an argumentation-based diagnostic Clinical Decision Support System (CDSS) has not been evaluated yet. The first part of this thesis evaluates the impact of guideline recommendations and an argumentation-based diagnostic CDSS on clinician information gathering and diagnostic decision making. In addition, the impact of guideline recommendations on management decision making was evaluated. The study found that argumentation is a viable method for generating diagnostic recommendations that can potentially help reduce diagnostic error. The study showed that guideline recommendations do have a positive impact on information gathering of optometrists and can potentially help optometrists in asking the right questions and performing tests as per current standards of care. Guideline recommendations were found to have a positive impact on management decision making. The CDSS is dependent on quality of data that is entered into the system. Faulty interpretation of data can lead the clinician to enter wrong data and cause the CDSS to provide wrong recommendations. Current generation argumentation-based CDSSs and other diagnostic decision support systems have problems with semantic interoperability that prevents them from using data from the web. The clinician and CDSS is limited to information collected during a clinical encounter and cannot access information on the web that could be relevant to a patient. This is due to the distributed nature of medical information and lack of semantic interoperability between healthcare systems. Current argumentation-based decision support applications require specialized tools for modelling and execution and this prevents widespread use and adoption of these tools especially when these tools require additional training and licensing arrangements. Semantic web and linked data technologies have been developed to overcome problems with semantic interoperability on the web. Ontology-based diagnostic CDSS applications have been developed using semantic web technology to overcome problems with semantic interoperability of healthcare data in decision support applications. However, these models have problems with expressiveness, requiring specialized software and algorithms for generating diagnostic recommendations. The second part of this thesis describes the development of an argumentation-based ontology driven diagnostic model and CDSS that can execute this model to generate ranked diagnostic recommendations. This novel model called the Disease-Symptom Model combines strengths of argumentation with strengths of semantic web technology. The model allows the domain expert to model arguments favouring and negating a diagnosis using OWL/RDF language. The model uses a simple weighting scheme that represents the degree of support of each argument within the model. The model uses SPARQL to sum weights and produce a ranked diagnostic recommendation. The model can provide justifications for each recommendation in a manner that clinicians can easily understand. CDSS prototypes that can execute this ontology model to generate diagnostic recommendations were developed. The decision support prototypes demonstrated the ability to use a wide variety of data and access remote data sources using linked data technologies to generate recommendations. The thesis was able to demonstrate the development of an argumentation-based ontology driven diagnostic decision support model and decision support system that can integrate information from a variety of sources to generate diagnostic recommendations. This decision support application was developed without the use of specialized software and tools for modelling and execution, while using a simple modelling method. The third part of this thesis details evaluation of the Disease-Symptom model across all stages of a clinical encounter by comparing the performance of the model with clinicians. The evaluation showed that the Disease-Symptom Model can provide a ranked diagnostic recommendation in early stages of the clinical encounter that is comparable to clinicians. The diagnostic performance can be improved in the early stages using linked data technologies to incorporate more information into the decision making. With limited information, depending on the type of case, the performance of the Disease-Symptom Model will vary. As more information is collected during the clinical encounter the decision support application can provide recommendations that is comparable to clinicians recruited for the study. The evaluation showed that even with a simple weighting and summation method used in the Disease- Symptom Model the diagnostic ranking was comparable to dentists. With limited information in the early stages of the clinical encounter the Disease-Symptom Model was able to provide an accurately ranked diagnostic recommendation validating the model and methods used in this thesis.
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Davidzon, Guido Alejandro. "Using EMR transactional data for personalize clinical decision support." Thesis, Massachusetts Institute of Technology, 2010. http://hdl.handle.net/1721.1/57687.
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Thesis (S.M.)--Harvard-MIT Division of Health Sciences and Technology, 2010.Cataloged from PDF version of thesis.
Includes bibliographical references (p. 41-44).
Collective intelligence techniques have been used to predict stock prices, customer purchasing habits, movies and books preferences for years, yet they remain unused in the medical profession. With the increasing adoption of electronic medical records, patients' medical data has grown exponentially and thus constitutes an untapped field where similar techniques could be applied. If data were collectively farmed and intelligently filtered, patient information could be added to traditional clinical decision support tools to arrive at personalized recommendations based on empiric evidence. The aim of this work is to use the collective, de facto, clinical experience to augment clinical guidelines thereby providing physicians with personalized clinical decision support. The pharmacological treatment of hypertension was chosen as the clinical domain in which to explore the feasibility of this approach. Twelve-thousand-three-hundred-forty-seven hypertensive patients were seen at the Internal Medical Associates (IMA) clinic at Massachusetts General Hospital (MGH) between July 2004 and September 2009. Their relevant clinical and demographic variables, drug regimens and blood pressure measurements were collected from the clinic's electronic medical record system and a dataset was generated. Back-end application software that draws upon case-based reasoning (CBR) was constructed and used to compute similarity between an index patient and existing hypertension patients.
(cont.) This program returned information regarding blood pressure control status and successful drug regimens used by similar patients. The use of EMR transactional data to provide a collective experience decision support (CEDSS) at the point-of-care using a computerized CBR approach is both technically possible and promising. Further studies are needed to evaluate the effectiveness of this method.
by Guido Alejandro Davidzon.
S.M.
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Orefors, Emil, and Nouri Issaki. "AI IN CONTEXT BASED STATISTICS IN CLINICAL DECISION SUPPORT." Thesis, Mälardalens högskola, Akademin för innovation, design och teknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-39923.
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Some treatments may cause unwanted effects and may make it difficult to achieve an optimal personalised decision for a specific patient. Decision support systems in healthcare is a topic that is getting much attention today. The purpose of using such a system is to enhance treatment's quality and to make it easier for clinicians to process and providing information by having access to patient's electronic health record and past experience. In this thesis, the developed a Clinical decision support system (CDSS) that helps clinicians to identify similar patients and extracting relevant experience. The vision here is to enable clinicians to make more informed decisions when choosing a suitable treatment for patient’s condition. So, here we focus on a more generic approach using case-based reasoning (CBR) and clustering in order to enable context-based statistics for a wider usage of CDSS in healthcare. We are testing our framework on a specific register that considers patients with cerebral pares and their ability to walk. In addition, the solution in our framework will measure how much the range of motions during the foot changes (increase or decrease) before and after an operation of the patient. During this work, an interview has been conducted with a clinical expert to collect requirements to develop such systems. The main function of the system is to check if a patient is similar to any previous patients so the clinician can get relevant information in choosing better treatment solution for a patient. The clinician involved in the project was convinced that our approach could become a valuable tool in a clinical decision-making situation.
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Spencer, Malik. "CHRISTINE: A Flexible Web-Based Clinical Decision Support System." University of Cincinnati / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1282052336.
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Murley, David Neil. "An ontological and epistemological paradigm for clinical decision support." Thesis, City University London, 1996. http://openaccess.city.ac.uk/12052/.
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This thesis sets out to test the central hypothesis that the paradigm for clinical decision support needs to shift from a technology centred paradigm to a coherent ontological-epistemological paradigm. This was achieved by formalising a coherent ontological and epistemological framework, and then applying it practically to clinical decision support. Initially the thesis reviews the need for a coherent philosophy in clinical decision support. It then goes on to describe the systematic analysis of established fundamental principles of philosophy, and the formalism of the ontological and epistemological framework. Following this the framework is applied to an analysis of clinical decision making and clinical decision support. The models derived from the analysis are then applied practically to the modelling of the management of acute renal failure patients in the intensive care setting. The results of this modelling are then combined with the decision models as the basis for the structure of a model of the decision making which controls the patient's renal replacement therapy. Finally the models representing the clinical problem and the clinical decision making process are used in the design and development of a prototype renal replacement therapy management system. The thesis concludes that a coherent ontological and epistemological framework provides clarity and insight during the analysis for and design of clinical decision support tools. The contributions of the thesis relate to the derivation and application of the framework, and the development of the renal replacement therapy management system. Thus the thesis is a foundation for future research in these two areas.
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Steury, Erin E. "Group emotions and social support." [Bloomington, Ind.] : Indiana University, 2007. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3278475.
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Thesis (Ph.D.)--Indiana University, Dept. of Psychology, 2007.Source: Dissertation Abstracts International, Volume: 68-10, Section: B, page: 7026. Adviser: Edward R. Hirt. Title from dissertation home page (viewed May 21, 2008).
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Sladeczek, Ingrid Edith. "Hardiness, social support, depression, and behavioral repertoire." Diss., The University of Arizona, 1993. http://hdl.handle.net/10150/186199.
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The purpose of this study was to examine the relationships among hardiness, social support, depression, and behavioral repertoire. One-hundred-sixty-five college students participated in the study. The hypothesis that hardiness and social support influence severity of depression and behavioral repertoire, and that depression is also causally related to behavioral repertoire was not supported. Instead, a parsimonious version of the initial hypothesized model was found to be a good representation of the data. The preferred model suggests that the personality characteristic, hardiness, is causally related to severity of depression, and furthermore, that social support directly influences behavioral repertoire. The findings are discussed in light of how they augment previous research and suggest new avenues for future research and practice.
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Anand, Sarabjot Singh. "Value-adding intelligence in clinical prognostic systems." Thesis, University of Ulster, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.311514.
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Pedersen, Kim Ohme. "Explanation Methods in Clinical Decision Support : A Hybrid System Approach." Thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for datateknikk og informasjonsvitenskap, 2010. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-11833.
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The use of computer-based decision support systems within the field of health science has over the last decades been extensively researched and tested, both in controlled environments and in clinical practice. Despite the obvious benefits of utilizing such systems in the day-to-day activities, many of the designed systems fail to make the impact one could hope to achieve. We have designed and implemented a prototype of a decision support system which use both Case-Based Reasoning and probabilistic inference through a Bayesian Network as a basis for the solution. To achieve user acceptance an explanation module has been implemented which gives the user full access to the data which has been used in the reasoning process, both from the Case-Based Reasoning and the Bayesian Network. The system has shown promising results within the domain of wine recommendation, with a very high accuracy despite uncertain accuracy of the knowledge within the system. Furthermore the explanations presented to an expert conformed to the causal way of reasoning used by said expert, and was accepted as a very useful tool to get pointed in the right direction for evaluation of the solution.
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Abeysinghe, Geetha Kalyani. "Event calculus to support temporal reasoning in a clinical domain." Thesis, University of Southampton, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.238888.
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Szymanski, Jacek. "AN INTEGRATED INFORMATICS INFRASTRUCTURE FOR PRE-CLINICAL RESEARCH-IT SUPPORT." Case Western Reserve University School of Graduate Studies / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=case1196266590.
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Kong, Guilan. "An online belief rule-based group clinical decision support system." Thesis, University of Manchester, 2011. https://www.research.manchester.ac.uk/portal/en/theses/an-online-belief-rulebased-group-clinical-decision-support-system(c31a65c7-60c3-4e7a-b18e-44fee95f7da1).html.
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Around ten percent of patients admitted to National Health Service (NHS) hospitals have experienced a patient safety incident, and an important reason for the high rate of patient safety incidents is medical errors. Research shows that appropriate increase in the use of clinical decision support systems (CDSSs) could help to reduce medical errors and result in substantial improvement in patient safety. However several barriers continue to impede the effective implementation of CDSSs in clinical settings, among which representation of and reasoning about medical knowledge particularly under uncertainty are areas that require refined methodologies and techniques. Particularly, the knowledge base in a CDSS needs to be updated automatically based on accumulated clinical cases to provide evidence-based clinical decision support. In the research, we employed the recently developed belief Rule-base Inference Methodology using the Evidential Reasoning approach (RIMER) for design and development of an online belief rule-based group CDSS prototype. In the system, belief rule base (BRB) was used to model uncertain clinical domain knowledge, the evidential reasoning (ER) approach was employed to build inference engine, a BRB training module was developed for learning the BRB through accumulated clinical cases, and an online discussion forum together with an ER-based group preferences aggregation tool were developed for providing online clinical group decision support.We used a set of simulated patients in cardiac chest pain provided by our research collaborators in Manchester Royal Infirmary to validate the developed online belief rule-based CDSS prototype. The results show that the prototype can provide reliable diagnosis recommendations and the diagnostic performance of the system can be improved significantly after training BRB using accumulated clinical cases.
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Van, Dyke Benjamin Prime. "Does perceived social support mediate the relationship between catastrophizing and pain response?" Thesis, The University of Alabama, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=1600906.
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Objective: The objective of the current investigation was to attempt to contribute to the limited body of literature regarding the role and importance of social context, in particular social support, to the experience of pain. Specifically, the current study proposed that some of the previously observed effects of pain catastrophizing on pain experience operate through a social mechanism of action by reducing available social resources to help people cope with the stress of pain. Thus, it was hypothesized that perceived social support would at least partially mediate the relationship between pain catastrophizing and pain experience or response variables (perceptions of pain intensity and unpleasantness and tolerance time).
Method: A sample of 285 college students enrolled in introductory psychology courses answered baseline questionnaires about dispositional variables before completing a fixed-time cold pressor task (60s) followed by an open-ended cold pressor task (to test pain tolerance). Questionnaires assessed perceived general social support (the Social Provisions Scale), dispositional and situational pain catastrophizing (Pain Catastrophizing Scale), and perceptions of pain intensity and unpleasantness (using visual analog scales).
Results: The final sample consisted of 285 college students (Age: M = 19.62, SD = 1.73), was primarily White (77.9%) and female (58.2%), and the majority denied having either chronic or recurrent pain (n = 160, 56.1%). Regression and SEM analyses demonstrated that the study mediation hypothesis was not supported.
Conclusion: Results of the current study suggest important revisions and considerations for future replications and similar studies.
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Kling, Michael Patrick. "Needs Assessment for Mental Health Support Towards Emergency Medical Service (EMS) Personnel." Thesis, Regent University, 2021. http://pqdtopen.proquest.com/#viewpdf?dispub=27961789.
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Understanding and assessing the needs of Emergency Medical Service (EMS) personnel and other first responders is crucial for providing these individuals with the resources needed within their community. The literature discusses how EMS personnel are at risk for psychological impairment due to routine exposure to traumatic events and occupational stressors within EMS organizations. Additionally, the research has supported the importance of positive coping abilities, organizational belongingness, and social support within the lives of EMS personnel to enable them to resiliently handle the occupational stress of their job. This study investigated the occupational needs of EMS providers to determine if they are receiving resources within their organization to cope with occupational stressors. Participants for this study comprised (n=153) paramedics and fire-fighters from the Tidewater EMS Council organization. A needs assessment was conducted to explore correlations between quality of life, resiliency, years of service, level of education, burnout, secondary traumatic stress, interpersonal support, positive and negative religious coping, and the occupational needs of EMS personnel. The results revealed that burnout (r=4.27**) and secondary traumatic stress (r.215*) were important factors for determining occupational turnover among EMS personnel. Furthermore, EMS providers reported occupational needs such as easier access to mental health, improved staff relations, adequate staffing, and improved shift hours are needed within their organization. Future research should explore differences in occupational needs with EMS providers among EMS organizations in metropolitan and rural communities. Keywords: Emergency Medical Services (EMS), Burnout, Occupational Stress, Traumatic Critical Incidents
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Glynn, M. J. "The implications of insulin and protein metabolism for clinical nutritional support." Thesis, University of Cambridge, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.599445.
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Washington, Tiffany K. "The Effects of Using Clinical Support Tools to Prevent Treatment Failure." BYU ScholarsArchive, 2010. https://scholarsarchive.byu.edu/etd/2459.
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To date, outcome research suggests that providing clinicians with patient progress feedback and problem-solving tools is effective in improving therapeutic outcome for clients who are predicted to have a negative treatment outcome. To expand upon this body of research, the current study examined the efficacy of using these problem-solving tools (Clinical Support Tools; CST) to reduce the risk of treatment failure and enhance positive outcome with 118 clients who were not identified as at -risk for a negative outcome. Results of this study indicated that the intervention failed to lower the rate of becoming an at-risk case or to enhance treatment outcome. A possible explanation for the null results observed is poor treatment compliance. Based on the findings of this study, the CST cannot be recommended as an intervention across the broad range of clients who enter treatment. However, qualitative analysis results reflect positive indicators for continued research with at-risk cases.
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Cheng, Lily. "Physiological evidence in support of music intervention in a clinical environment." Thesis, Boston University, 2013. https://hdl.handle.net/2144/12071.
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Thesis (M.A.)--Boston UniversityIntegrative medicine is the synergistic ability of alternative therapy to complement evidence-based medicine. Therapeutic music can induce changes in mood state and physiology and be useful in stressful clinical environments and in the recovery of patients. In an extensive literature review, experiments recounting the various physiological effects of music were analyzed to support the use of music interventions. Results demonstrated a significant influence on IgA, IL-6, epinephrine, norepinephrine, serotonin, growth hormone, cortisol, estrogen, testosterone, oxytocin, and prolactin concentrations. By altering autonomic activity and subsequent changes in the hypothalamic-pituitary axis, music influences hemodynamics, alleviates perception of pain and anxiety and reduces the body’s stress response to severe injury. Data retrieved by summarized studies lend support to prescribed music interventions to patients.
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AMARAL, Thiago Magalhães. "Optimal control in biological systems as a support for clinical decisions." Universidade Federal de Pernambuco, 2009. https://repositorio.ufpe.br/handle/123456789/6002.
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O controle ótimo no mundo biológico tem uma vasta aplicação em incontáveis sistemas os quais influenciam enormemente nossas vidas. Objetiva-se a aplicação desta ferramenta em dois sistemas. O primeiro diz respeito ao controle ótimo de dosagem de drogas no tratamento de pacientes infectados pelo vírus HIV . O modelo de Campello de Souza (1999) é usado para estimar a dosagem de drogas onde a função objetivo é minimizada. Esta função representa um balanço entre os benefícios do tratamento e os efeitos colaterais. A técnica de controle ótimo usada é o Princípio do Máximo de Pontryagin, a qual é simulada através do PROPT-TOMLAB - Matlab Optimal Control System Software em uma versão de demonstração. As simulações objetivam a análise de três diferentes pacientes em dois diferentes cenários. Estes cenários têm como objetivo forçar as variáveis de estado a atingirem valores "normais" a fim de estabilizar a carga viral próximo a uma taxa que seja insignificante e elevar o nível de CD4 do paciente. São simulados tratamentos cedos e tardios. As simulações computacionais compararam diferentes cenários para investigar os parâmetros de incerteza da dinâmica entre o vírus HIV e os linfócitos CD4 e CD8. Os resultados mostram que o controle ótimo permite uma melhor administração entre os efeitos positivos da terapia e os efeitos colaterais, ao invés de se usar dosagens constantes de drogas como na atual prática médica. O segundo sistema descreve a aplicação do controle ótimo, também através do Princípio Máximo de Pontryagin, para controlar o nível de glicose em indivíduos diabéticos usando o modelo matemático desenvolvido por Bergman (1971, 1981). Correlacionam-se dados reais da literatura com o modelo teórico para analisar a robustez do modelo. É também estudada a minimização do funcional objetivo para diminuir os efeitos colaterais e consequentemente melhorar o estado de saúde do paciente. Os resultados mostram os benefícios de se utilizar o controle ótimo para regular a taxa de glicose em pacientes diabéticos
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Bennasar, Mohamed. "Clinical decision support system for early detection and diagnosis of dementia." Thesis, Cardiff University, 2014. http://orca.cf.ac.uk/73073/.
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Dementia is a syndrome caused by a chronic or progressive disease of the brain, which affects memory, orientation, thinking, calculation, learning ability and language. Until recently, early diagnosis of dementia was not a high priority, since the related diseases were considered untreatable and irreversible. However, more effective treatments are becoming available, which can slow the progress of dementia if they are used in the early stages of the disease. Therefore, early diagnosis is becoming more important. The Clock Drawing Test (CDT) and Mini Mental State Examination (MMSE) are well-known cognitive assessment tests. A known obstacle to the wider usage of the CDT assessments is the scoring and interpretation of the results. This thesis introduces a novel diagnostic Clinical Decision Support System (CDSS) based on CDT which can help in the diagnosis of three stages of dementia. It also introduces the advanced methods developed for the interpretation and analysis of CDTs. The data used in this research consist of 604 clock drawings produced by dementia patients and healthy individuals. A comprehensive catalogue of 47 visual features within CDT drawings is proposed to enhance the sensitivity of the CDT in diagnosing the early stages of dementia. These features are selected following a comprehensive analysis of the available data and the most common CDT scoring systems reported in the medical literature. These features are used to build a new digitised dataset necessary for training and validating the proposed CDSS. In this thesis, a novel feature selection method is proposed for the study of CDT feature significance and to define the most important features in diagnosing dementia. iii A new framework is also introduced to analyse the temporal changes in the CDT features corresponding to the progress of dementia over time, and to define the first onset symptoms. The proposed CDSS is designed to differentiate between four cognitive function statuses: (i) normal; (ii) mild cognitive impairment or mild dementia; (iii) moderate or severe dementia; and (vi) functional. This represents a new application of the CDT, as it was previously used only to detect the positive dementia cases. Diagnosing mild cognitive impairment or early stage dementia using CDT as a standalone tool is a very challenging task. To address this, a novel cascade classifier is proposed, which benefits from combining CDT and MMSE to enhance the overall performance of the system. The proposed CDSS diagnoses the CDT drawings and places them into one of three cognitive statuses (normal or functional, mild cognitive impairment or mild dementia, and moderate or severe dementia) with an accuracy of 78.34 %. Moreover, the proposed CDSS can distinguish between the normal and the abnormal cases with accuracy of 89.54 %. The achieved results are good and outperform most of CDT scoring systems in discriminating between normal and abnormal cases as reported in existing literature. Moreover, the system shows a good performance in diagnosing the CDT drawings into one of the three cognitive statuses, even comparing well with the performance of dementia specialists. The research has been granted ethical approval from the South East Wales Research Ethics Committee to employ anonymised copies of clock drawings and copies of Mini Mental State Examination made by patients during their examination by the memory team in Llandough hospital, Cardiff.
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Williams, C. Lesley. "A computer-based decision support system for orthodontic diagnosis and treatment planning." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp04/mq21223.pdf.
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Khargi, Shiromanie Sharla. "Caregiver Support and Advocacy for Children with Disabilities in Guyana." ScholarWorks, 2018. https://scholarworks.waldenu.edu/dissertations/5887.
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Abstract
In Guyana, a developing country in South America, many parental caregivers of disabled people struggle to understand their children's disability as well as provide, and advocate for, educational resources and medical and psychological care for their children. The researchers have found that the needs of this population have been minimized placing disabled people at risk for neglect, abuse, and death. In 2006, the Convention on the Rights for People with Disabilities created a plan to help developing countries improve the care and advocacy for people with disabilities. The purpose of this evaluative study, which was guided by general systems theory, was to examine and assess whether the Community Based Rehabilitation Program serves caregivers of the disabled population in Guyana effectively, identify the positive and negative aspects of the program, and recommend improvements to the program. Qualitative research methods were used. Surveys and interviews were administered to 73 caregivers of disabled people. Data were analyzed using triangulation strategies for data verification to identify specific themes. The findings of this study indicated that caregivers of people with disabilities are in dire need of educational resources, support groups, and training. They also suggested that when provided relevant information, caregivers tend to feel more empowered to serve as agents of care for their disabled children. Results from the study may spur policy makers to implement relevant training for Guyanese caregivers and provide them with necessary resources, which may lead to improvements in the lives of disabled people in Guyana and the possibility for social justice.
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Bravi, Andrea. "Variability Monitoring for Clinical Applications." Thèse, Université d'Ottawa / University of Ottawa, 2014. http://hdl.handle.net/10393/31116.
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Current monitoring tools in the intensive care units focus on displaying physiologically monitored parameters (e.g. vital signs such as heart rate, respiratory rate and blood pressure) at the present moment. Added clinical utility can be found by analyzing how the conditions of a patient evolve with time, and automatically relating that dynamics to population trends. Variability analysis consists of monitoring patterns of variation over intervals in time of physiological signals such as heart rate and respiratory rate. Given that illness has been associated in multiple studies with altered variability, most commonly lack of variation, variability monitoring represents a tool whose contribution at the bedside still needs to be explored. With the long term objective of improving care, this thesis promotes the use of variability analysis through three distinct types of analysis: facing the technical challenges involved with the dimensionality of variability analysis, enhancing the physiological understanding of variability, and showing its utility in real world clinical applications. In particular, the contributions of this thesis include: the review and classification into domains of a large array of measures of variability; the design of system and methods to integrate multiple measures of variability into a unique score, called composite measure, bringing relevant information to specific clinical problems; the comparison of patterns of heart rate variability during exercise and sepsis development, showing the inability of single measures of variability to discriminate between the two kinds of stressors; the analysis of variability produced from a physiologically-based model of the cardiovascular system, showing that each single measure of variability is an unspecific sensor of the body, thereby promoting multivariate analysis to the only means of understanding the physiology underlying variability; the study of heart rate variability in a population at high risk of sepsis development, showing the ability of variability to predict the occurrence of sepsis more than 48 hours in advance respect to the time of diagnosis of the clinical team; the study of heart and respiratory rate variability in intubated intensive care unit patients, showing how variability can provide a better way of assessing extubation readiness respect to commonly used clinical parameters. Overall, it is hoped that these novel contributions will help promoting bedside applications of variability monitoring to improve patient care.
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Brooks, Jennifer Margaret Beckett. "Personality style, psychological adaptation and expectations of psychologists in clinical training." Thesis, Open University, 1999. http://oro.open.ac.uk/57920/.
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Objectives: The current study aimed to profile the personality styles, expectations and psychological adaptation of Clinical Psychology Trainees. It also aimed to look at the relationship between these variables. Design: A cross-sectional postal questionnaire study, employing between group and correlational analyses. Methods: A sample of 364 psychologists in clinical training (57% response rate) from 15 UK clinical psychology training courses participated in the study. They completed questionnaires of personality, psychological adaptation, social support and an expectations measure specifically designed for the study. Results: The majority of psychologists in clinical training who participated in the study were well adjusted in terms of personality, did not experience extensive problems with psychological adaptation, and had the majority of their expectations met. A significant sub group reported personality adjustment problems and problems with self esteem, anxiety, depression and work adjustment. Low self esteem was present in just under a quarter of the sample. Personality adjustment was found to be related to expectations and psychological adaptation. Trainee psychologists with poorer personality adjustment were less likely to have their expectations met, especially with regard to the impact of training on their life, and were more likely to suffer from poor psychological adaptation, particularly in terms of low self esteem, anxiety, depression and work adjustment problems. Self esteem was related to discrepancies in actual and ideal social support. Some differences were found between year groups. Gender and age were not related to personality adjustment, psychological adaptation or expectations. Conclusions and Implications: The findings were discussed in terms of the interpretation of personality style. Implications for clinical psychology training and the profession of clinical psychology were considered.
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Monson, Eva. "Quality of life and social support in relation to trauma and posttraumatic stress disorder." Thesis, McGill University, 2012. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=106311.
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Research in the areas of quality of life and social support suggests a complex relationship with trauma. Most studies have found inverse relationships between both quality of life and social support posttrauma. However, it remains unclear how these findings apply to individuals with a current or past diagnosis of posttraumatic stress disorder (PTSD), or to individuals who do not meet the criteria for PTSD. Furthermore, previous research has been limited to global index outcomes, which overlook specific domains that could reveal more information, obtained from convenience samples. We seek here to address these limitations by analysing data from a large community-based epidemiological catchment area study conducted in the south west of Montreal (N = 2399) broken down according to current PTSD diagnostic status: (i) Current PTSD, (ii) Past PTSD, (iii) No lifetime PTSD in spite of trauma exposure, and (iv) No lifetime trauma exposure. The inverse relationship of decreased quality of life posttrauma, specifically as PTSD severity increases, was replicated within our sample. In contrast to many previous studies, we show that social support was not significantly different between PTSD and No PTSD groups. Subscale scores were examined in depth for both quality of life and social support, with outcomes showing that even after PTSD remits, the gap between PTSD and No PTSD groups widens. This research allows for a much needed broadening of our understanding of quality of life for individuals with current and remitted PTSD.Les recherches dans les domaines de la qualité de vie et du soutien social suggèrent une relation complexe avec le trauma. La plupart des études ont montré une relation inverse entre la qualité de vie et le soutien social après avoir eu une expérience traumatique. Cependant, il n'est pas encore certain que ces résultats s'appliquent à des personnes présentant un diagnostic actuel ou passé de trouble de stress post-traumatique (TSPT), ou à des personnes qui ne présentent pas de TSPT. Par ailleurs, ces résultats se rapportent à des indices globaux, qui négligent des domaines spécifiques qui pourraient donner davantage d'information, obtenus à partir d'échantillons de commodité. Nous cherchons ici à remédier à ces limites en analysant les données d'une grande base communautaire issue d'une étude épidémiologique du sud-ouest de Montréal (N =2399) répartis selon leur état de TSPT courant: (i) le TSPT courant, (ii) le TSPT passé, (iii) aucun diagnostic de TSPT en dépit des événements traumatiques, et (iv) aucune exposition à un événement traumatique à vie. La relation inverse de la diminution de la qualité de vie, spécifiquement quand les symptômes de TSPT augmentent, a été reproduite dans notre échantillon. Contrairement à de nombreuses études antérieures, nous montrons que le soutien social n'a pas été significativement différent en comparant les groupes avec TSPT et les groupes sans TSPT. Les résultats des sous-échelles ont été également examinés pour la qualité de vie et le soutien social, avec des résultats montrant que même lorsque le TSPT est passé, le fossé entre le groupe avec TSPT et le groupe sans TSPT s'élargit. Cette recherche permet d'améliorer grandement notre compréhension de la qualité de vie des personnes souffrant de TSPT courant et remis.
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Benjamin, Ereshia. "Coping with mental illness : a case study in initiating a support group in Mamre." Master's thesis, University of Cape Town, 1993. http://hdl.handle.net/11427/13462.
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Bibliography: leaves 58-64.The aim of this dissertation is to provide a detailed description and partial evaluation of a support group for psychiatric outpatients and their relatives in Mamre. The first nineteen sessions of the support group are described in detail, and the evaluation information was obtained from the participants at the evaluation workshop held in February 1992, as well as the facilitator's observations. The nineteen group sessions were usually held once. a month, .but not every month, between June 1990 and February 1992. The sessions were all held on Sunday afternoons and usually lasted two to two-and-a-half hours. · All known psychiatric out-patients in Mamre and their families were invited to all the meetings. Attendance varied, but a group of approximately 11 regular attenders emerged over the nineteen sessions. Three community workers also joined the group at various stages and became regular participants in the group. The preliminary evaluation of the group revealed that the group is functioning within the working phase of group development, and that the group provided a number of benefits to the regular participants. The evaluation also highlighted issues of autonomy, heterogeneity and non-adherence. Suggestions for further research and group aims, focusing around these issues, are made.
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Daniels, Aneesa. "An evaluation of the Learning Support Group programme at the UCT Child Guidance Clinic." Master's thesis, University of Cape Town, 2008. http://hdl.handle.net/11427/10395.
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Includes abstract.Includes bibliographical references (leaves 95-107).
The objective of the Learning Support Group (LSO) programme at the UCT Child Guidance Clinic (COC) is to develop the academic and psychosocial functioning of children with learning difficulties. The programme operates on the rationale that remediation alone cannot address the multiple needs of the learning-disordered child, and that psychotherapeutic intervention for the child and for his or her parents may enhance the 'child's academic, emotional and behavioural functioning. This study evaluated the programme outcomes for the child participants (n=6), and for the participating parents (n=9), of the 2006 LSO programme. A single-group pre- and post-measures design was used. Data were triangulated from multiple sources including pre- and post-intervention semi-structured interviews with the children and with their parents, qualitative reports from the children's teachers, parents' and teachers' ratings of the children's cognitive and behaviour problems on the Conners' Rating Scales - Revised, the children's school reports, and scholastic tests conducted by the LSO remedial teacher. A combination of qualitative and quantitative analytic methods were employed. Findings from the evaluation indicated a statistically significant improvement in the children's academic functioning and in their problem behaviours, and qualitative reports of progress in their social relationships. The participating parents' reported that their knowledge and understanding of learning disorders had increased, alongside the acquisition of parenting and homework strategies. Parents also experienced positive shifts in their parenting style and in their relationship with their child. Given a number of methodological limitations, the extent to which the LSO intervention is responsible for these shifts cannot be conclusively established, however, the findings suggest considerable promise for its efficacy. Recommendations for the evaluation of similar programmes are offered, based on the lessons learned from the current study.
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Correa, Veronica M. "The impact of role stress, self-efficacy, organizational support, and supervisory support on performance in school-based mental health trainees." Thesis, Alliant International University, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=3681796.
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There is limited understanding of the variables that impact trainee performance in clinical settings. As such, investigating the tools used to assess performance was warranted. In the first part of the present research study, I investigated the independent factors that comprised the FPEF using 294 archival forms completed by clinical supervisors at the California School of Professional Psychology, Alliant International University, San Francisco. The five conceptually derived domains of clinical competency of the FPEF included: psychological intake, evaluation and assessment; clinical interventions; professional roles and behaviors; self-examination and development; and supervision. A principal factor analysis was conducted to determine whether the items of the FPEF comprised performance factors that were based on these FPEF domains. Results yielded a four-factor solution based on how the items clustered together. Thus, subscales were retitled as follows: Clinical Development, Professional Roles and Behaviors, Psychological Conceptualization and Intervention, and Psychological Assessment Skills. The second part of the present study included trainee self-ratings and supervisor ratings on the FPEFs for 47 school-based mental health trainees. This part of the study focused on whether the internal psychological variables of role conflict, role ambiguity, and self-efficacy (domain specific), and the external variables of both organizational support and supervisory support were associated with performance ratings on the FPEF. Findings indicated that the internal and external variables were both associated with trainees' performances across various domains. However, some of these relationships were contrary to what was expected. Organizational support and supervisory support were found to have negative associations with specific performance domains, which may have been due to limitations of the instruments or moderating variables that were not measured in this study.
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Govender, Kevashini. "Gender dynamics of a Parents Support Group." Master's thesis, University of Cape Town, 2005. http://hdl.handle.net/11427/8036.
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Using a Parents Support Group run in Cape Town, South Africa, the study aims to illustrate how the psychodynamic model is inadequate in providing an understanding of the gender dynamics within this group. The study takes on an interpretive approach to explore this research problem and employs qualitative techniques, which were found to be most suitable. Data were collected through the use of field notes that documented the process of the Parents Support Group. This group consisted of ten females and one male participant, ranging from 34 to 45 years of age and who come from a lower socio-economic stratum. Furthermore, to elicit additional data and to capture the multi-layered experienees of participants, semi-structured interviews were conducted with five female and one male participant who were selected from the Parent Support Group. The field notes and the data acquired from the interviews were analyzed through the method of textual analysis, which elicited five key themes. It was found that although the psychoanalytic model was inadequate in explaining certain dynamics within the Parents Support Group, feminism and social constructionism models were able to highlight how gender was key in the dynamics at play within the group. This finding was vital in making recommendations about gender sensitivity and training concerning therapeutic interventions.
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Nogueira, Mariana. "Machine learning to support exploring and exploiting real-world clinical longitudinal data." Doctoral thesis, Universitat Pompeu Fabra, 2020. http://hdl.handle.net/10803/669968.
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Following-up on patient evolution by reacquiring the same measurements over time (longitudinal data) is a crucial component in clinical care dynamics, as it creates opportunity for timely decision making in preventing adverse outcome. It is thus important that clinicians have proper longitudinal analysis tools at their service. Nonetheless, most traditional longitudinal analysis tools have limited applicability if data are (1) not highly standardized or (2) very heterogeneous (e.g. images, signal, continuous and categorical variables) and/or high-dimensional. These limitations are extremely relevant, as both scenarios are prevalent in routine clinical practice.
The aim of this thesis is the development of tools that facilitate the integration and interpretation of complex and nonstandardized longitudinal clinical data. Specifically, we explore approaches based on unsupervised dimensionality reduction, which allow the integration of complex longitudinal data and their representation as low-dimensional yet clinically interpretable trajectories.
We showcase the potential of the proposed approach in the contexts of two specific clinical problems with different scopes and challenges: (1) nonstandardized stress echocardiography and (2) labour monitoring and decision making.
In the first application, the proposed approach proved to help in the identification of normal and abnormal patterns in cardiac response to stress and in the understanding of the underlying pathophysiological mechanisms, in a context of nonstandardized longitudinal data collection involving heterogeneous data streams. In the second application, we showed how the proposed approach could be used as the central concept of a personalized labour monitoring and decision support system, outperforming the current reference labour monitoring and decision support tool.
Overall, we believe that this thesis validates unsupervised dimensionality reduction as a promising approach to the analysis of complex and nonstandardized clinical longitudinal data.El seguimiento de la evolución de un paciente tomando las mismas medidas en diferentes instantes temporales (datos longitudinales) es un componente crucial en la dinámica de los cuidados médicos, ya que permite tomar decisiones correctas en el momento idóneo para prevenir eventos adversos. Es entonces importante que los médicos tengan a su disposicion herramientas para analizar datos de carácter longitudinal. Sin embargo, la mayoría de las herramientas que actualmente existen tienen una aplicabilidad limitada si los datos (1) no están suficientemente estandarizados o (2) son muy heterogéneos (eg: imágenes, señales, variables continuas y categóricas) y/o tienen una alta dimensionalidad. Estas limitaciones son tremendamente relevantes, ya que ambos casos son prevalentes en la practica clínica habitual. El objetivo de esta tesis es el desarrollo de herramientas que facilitan la integración e interpretación de datos clínicos longitudinales que son complejos y no están estandarizados. Específicamente, exploramos enfoques basados en la reducción de dimensionalidad no supervisada, que permite integrar datos longitudinales complejos y su representación como una trayectoria de baja dimensión que es clínicamente interpretable. Mostramos el potencial del enfoque propuesto en el contexto de dos problemas clínicos en diferentes ámbitos y con diferentes desafíos: (1) ecocardiografía de estrés no estandarizada y (2) monitoreo de parto y toma de decisiones. En la primera aplicación, el enfoque propuesto ha mostrado ser de ayuda en la identificación de patrones normales y anormales en la respuesta cardiaca al estrés y en entender los mecanismos patofisiologicos subyacentes, en el contexto de una adquisición de datos longitudinales no estandarizados que contiene un flujo de datos heterogéneo. En la segunda aplicación, mostramos como el enfoque propuesto puede ser el concepto central de un sistema de monitoreo del parto y soporte a la decisión personalizado, superando el sistema actual de referencia. En conclusión, creemos que esta tesis muestra que la reducción de dimensión no supervisada es un prometedor enfoque para analizar datos clínicos longitudinales complejos y no estandarizados.
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Elliman, Anthony David. "On the design of computer software to support clinical follow-up studies." Thesis, Brunel University, 1988. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.328954.
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Zimit, Sani Ibrahim. "Hybrid approach to interpretable multiple classifier system for intelligent clinical decision support." Thesis, University of Reading, 2013. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.631699.
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Data-driven decision support approaches have been increasingly employed in recent years in order to unveil useful diagnostic and prognostic patterns from data accumulated in clinical repositories. Given the diverse amount of evidence generated through everyday clinical practice and the exponential growth in the number of parameters accumulated in the data, the capability of finding purposeful task-oriented patterns from patient records is crucial for providing effective healthcare delivery. The application of classification decision support tool in clinical settings has brought about formidable challenges that require a robust system. Knowledge Discovery in Database (KDD) provides a viable solution to decipher implicit knowledge in a given context. KDD classification techniques create models of the accumulated data according to induction algorithms. Despite the availability of numerous classification techniques, the accuracy and interpretability of the decision model are fundamental in the decision processes. Multiple Classifier Systems (MCS) based on the aggregation of individual classifiers usually achieve better decision accuracy. The down size of such models is due to their black box nature. Description of the clinical concepts that influence each decision outcome is fundamental in clinical settings. To overcome this deficiency, the use of artificial data is one technique advocated by researchers to extract an interpretable classifier that mimics the MCS. In the clinical context, practical utilisation of the mimetic procedure depends on the appropriateness of the data generation method to reflect the complexities of the evidence domain. A well-defined intelligent data generation method is required to unveil associations and dependency relationships between various entities the evidence domain. This thesis has devised an Interpretable Multiple classifier system (IMC) using the KDD process as the underlying platform. The approach integrates the flexibility of MCS, the robustness of Bayesian network (BN) and the concept of mimetic classifier to build an interpretable classification system. The BN provides a robust and a clinically accepted formalism to generate synthetic data based on encoded joint relationships of the evidence space. The practical applicability of the IMC was evaluated against the conventional approach for inducing an interpretable classifier on nine clinical domain problems. Results of statistical tests substantiated that the IMC model outperforms the direct approach in terms of decision accuracy.
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Marshall, Stephen James. "The development of neural network based decision aids for clinical decision support." Thesis, University of Sheffield, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.245570.
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Nirantharakumar, Krishnarajah. "Clinical decision support systems in the care of hospitalised patients with diabetes." Thesis, University of Birmingham, 2013. http://etheses.bham.ac.uk//id/eprint/4734/.
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This thesis explored the role of health informatics (decision support systems) in caring for hospitalised patients with diabetes through a systematic review and by analysing data from University Hospital Birmingham, UK. Findings from the thesis: 1) highlight the potential role of computerised physician order entry system in improving guideline based anti-diabetic medication prescription in particular insulin prescription, and their effectiveness in contributing to better glycaemic control; 2) quantify the occurrence of missed discharge diagnostic codes for diabetes using electronic prescription data and suggests 60% of this could be potentially reduced using an algorithm that could be introduced as part of the information system; 3) found that hypoglycaemia and foot disease in hospitalised diabetes patients were independently associated with higher in-hospital mortality rates and longer length of stay; 4) quantify the hypoglycaemia rates in non-diabetic patients and proposes one method of establishing a surveillance system to identify non diabetic hypoglycaemic patients; and 5) introduces a prediction model that may be useful to identify patients with diabetes at risk of poor clinical outcomes during their hospital stay.
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Kanwal, Summrina. "Towards a novel medical diagnosis system for clinical decision support system applications." Thesis, University of Stirling, 2016. http://hdl.handle.net/1893/25397.
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Clinical diagnosis of chronic disease is a vital and challenging research problem which requires intensive clinical practice guidelines in order to ensure consistent and efficient patient care. Conventional medical diagnosis systems inculcate certain limitations, like complex diagnosis processes, lack of expertise, lack of well described procedures for conducting diagnoses, low computing skills, and so on. Automated clinical decision support system (CDSS) can help physicians and radiologists to overcome these challenges by combining the competency of radiologists and physicians with the capabilities of computers. CDSS depend on many techniques from the fields of image acquisition, image processing, pattern recognition, machine learning as well as optimization for medical data analysis to produce efficient diagnoses. In this dissertation, we discuss the current challenges in designing an efficient CDSS as well as a number of the latest techniques (while identifying best practices for each stage of the framework) to meet these challenges by finding informative patterns in the medical dataset, analysing them and building a descriptive model of the object of interest and thus aiding in medical diagnosis. To meet these challenges, we propose an extension of conventional clinical decision support system framework, by incorporating artificial immune network (AIN) based hyper-parameter optimization as integral part of it. We applied the conventional as well as optimized CDSS on four case studies (most of them comprise medical images) for efficient medical diagnosis and compared the results. The first key contribution is the novel application of a local energy-based shape histogram (LESH) as the feature set for the recognition of abnormalities in mammograms. We investigated the implication of this technique for the mammogram datasets of the Mammographic Image Analysis Society and INbreast. In the evaluation, regions of interest were extracted from the mammograms, their LESH features were calculated, and they were fed to support vector machine (SVM) and echo state network (ESN) classifiers. In addition, the impact of selecting a subset of LESH features based on the classification performance was also observed and benchmarked against a state-of-the-art wavelet based feature extraction method. The second key contribution is to apply the LESH technique to detect lung cancer. The JSRT Digital Image Database of chest radiographs was selected for research experimentation. Prior to LESH feature extraction, we enhanced the radiograph images using a contrast limited adaptive histogram equalization (CLAHE) approach. Selected state-of-the-art cognitive machine learning classifiers, namely the extreme learning machine (ELM), SVM and ESN, were then applied using the LESH extracted features to enable the efficient diagnosis of a correct medical state (the existence of benign or malignant cancer) in the x-ray images. Comparative simulation results, evaluated using the classification accuracy performance measure, were further benchmarked against state-of-the-art wavelet based features, and authenticated the distinct capability of our proposed framework for enhancing the diagnosis outcome. As the third contribution, this thesis presents a novel technique for detecting breast cancer in volumetric medical images based on a three-dimensional (3D) LESH model. It is a hybrid approach, and combines the 3D LESH feature extraction technique with machine learning classifiers to detect breast cancer from MRI images. The proposed system applies CLAHE to the MRI images before extracting the 3D LESH features. Furthermore, a selected subset of features is fed to a machine learning classifier, namely the SVM, ELM or ESN, to detect abnormalities and to distinguish between different stages of abnormality. The results indicate the high performance of the proposed system. When compared with the wavelet-based feature extraction technique, statistical analysis testifies to the significance of our proposed algorithm. The fourth contribution is a novel application of the (AIN) for optimizing machine learning classification algorithms as part of CDSS. We employed our proposed technique in conjunction with selected machine learning classifiers, namely the ELM, SVM and ESN, and validated it using the benchmark medical datasets of PIMA India diabetes and BUPA liver disorders, two-dimensional (2D) medical images, namely MIAS and INbreast and JSRT chest radiographs, as well as on the three-dimensional TCGA-BRCA breast MRI dataset. The results were investigated using the classification accuracy measure and the learning time. We also compared our methodology with the benchmarked multi-objective genetic algorithm (ES)-based optimization technique. The results authenticate the potential of the AIN optimised CDSS.
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Vicente, Robledo Javier. "Clinical Decision Support Systems for Brain Tumour Diagnosis: Classification and Evaluation Approaches." Doctoral thesis, Editorial Universitat Politècnica de València, 2012. http://hdl.handle.net/10251/17468.
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A lo largo de las últimas décadas, la disponibilidad cada vez mayor de grandes cantidades de información biomédica ha potenciado el desarrollo de herramientas que permiten extraer e inferir conocimiento. El aumento de tecnologías biomédicas que asisten a los expertos médicos en sus decisiones ha contribuido a la incorporación de un paradigma de medicina basada en la evidencia centrada en el paciente.
Las contribuciones de esta Tesis se centran en el desarrollo de herramientas que asisten al médico en su proceso de toma de decisiones en el diagnóstico de tumores cerebrales (TC) mediante Espectros de Resonancia Magnética (ERM).
En esta Tesis se contribuye con el desarrollo de clasificadores basados en Reconocimiento de Patrones (RP) entrenados con ERM de pacientes pediátricos y adultos para establecer el tipo y grado del tumor. Estos clasificadores especializados son capaces de aprovechar las diferencias bioquímicas existentes entre los TC infantiles y de adultos.
Una de las principales contribuciones de esta Tesis consiste en el desarrollo de modelos de clasificación enfocados a discriminar los tres tipos de tumores cerebrales pediátricos más prevalentes. El cerebelo suele ser una localización habitual de estos tumores, resultando muy difícil distinguir el tipo mediante el uso de Imagen de Resonancia Magnética. Por lo tanto, obtener un alto acierto en la discriminación de astrocitomas pilocíticos, ependimomas y meduloblastomas mediante ERM resulta crucial para establecer una estrategia de cirugía, ya que cada tipo de tumor requiere de unas acciones diferentes si se quiere obtener un buen pronóstico del paciente.
Asimismo, esta Tesis contribuye en la extracción de características para ERM mediante el estudio de la combinación de señales de ERM adquiridas en dos tiempos de eco: tiempo de eco corto y tiempo de eco largo; concluyendo que dicha combinación mejora la clasificación de tumores cerebrales pediátricos frente al hecho de usar únicamente los ERM de unVicente Robledo, J. (2012). Clinical Decision Support Systems for Brain Tumour Diagnosis: Classification and Evaluation Approaches [Tesis doctoral]. Editorial Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/17468
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