Gotowe bibliografie tematyczne / Medical Decision Support System (MDSS)

Spis treści

  1. Artykuły w czasopismach
  2. Rozprawy doktorskie
  3. Książki
  4. Części książek
  5. Streszczenia konferencji
  6. Raporty organizacyjne

Gotowa bibliografia na temat „Medical Decision Support System (MDSS)”

Autor: Grafiati
Data publikacji: 3 czerwca 2025
Data aktualizacji: 31 lipca 2025

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Artykuły w czasopismach na temat "Medical Decision Support System (MDSS)"

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Raghu, Babu Korrapati *. "COMPREHENSIVE RESEARCH MAP ON MEDICAL DECISION SUPPORT SYSTEMS (MDSS) – HISTORICAL EVOLUTION REVIEW." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 6, no. 5 (2017): 399–401. https://doi.org/10.5281/zenodo.573546.

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Medical Decision Support System (MDSS) plays an increasingly crucial role in medical practices as it assists physicians to make clinical decisions and thus MDSS are expected to improve the overall quality of medical care. Research in the field of MDSS has suggested the there is a rising need to understand and discuss various developments in the field and how they have enhanced the medical and healthcare sector. This paper will provide a research roadmap to various developments over the years in the field of MDSS.
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Sejwal, Lakshita. "A Machine Learning-based Framework for Medical Decision Support Systems." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 11, no. 1 (2020): 868–77. http://dx.doi.org/10.17762/turcomat.v11i1.13570.

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Due to the complexity of clinical decision-making and the necessity for patient-specific suggestions, medical decision support systems (MDSS) are becoming more significant in healthcare. Machine learning (ML) can analyze and learn from vast volumes of patient data, making it a strong tool for MDSS development. Domain specialists, data scientists, and software engineers must work together to produce MDSS. This study provides a Machine Learning-based MDSS development approach that prioritizes stakeholder collaboration. Medical Decision Support Systems (MDSS) provide tailored recommendations base
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Chuprov, A. D., I. P. Bolodurina, A. O. Lositskiy, et al. "Progression signs of retinal disease used to increase the validity of an artificial intelligence-based medical decision support system." Modern technologies in ophtalmology, no. 5 (October 6, 2023): 88–91. http://dx.doi.org/10.25276/2312-4911-2023-5-88-91.

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Purpose is to determine the most significant diagnostic signs of retinal disease progression, which are used to increase the validity of the medical decision support system (mdSS) for describing oct images using the method of an expert survey. Material and methods. An expert analysis of the binary signs used in mdSS when describing the oct image of the macular area was carried out: each sign was assessed in terms of the development of the pathological process, the need for telemedicine consultation and (or) referral of patients to a medical organization. The expert method included a survey of
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Jung, Se Young, Hee Hwang, Keehyuck Lee, et al. "Barriers and Facilitators to Implementation of Medication Decision Support Systems in Electronic Medical Records: Mixed Methods Approach Based on Structural Equation Modeling and Qualitative Analysis." JMIR Medical Informatics 8, no. 7 (2020): e18758. http://dx.doi.org/10.2196/18758.

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Background Adverse drug events (ADEs) resulting from medication error are some of the most common causes of iatrogenic injuries in hospitals. With the appropriate use of medication, ADEs can be prevented and ameliorated. Efforts to reduce medication errors and prevent ADEs have been made by implementing a medication decision support system (MDSS) in electronic health records (EHRs). However, physicians tend to override most MDSS alerts. Objective In order to improve MDSS functionality, we must understand what factors users consider essential for the successful implementation of an MDSS into th
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Bontsevich, R. A., V. K. Kotlyarova, O. A. Bukach, A. V. Gubarev, D. G. Dubonosova, and I. G. Rostovceva. "Analysis of treatment approaches and the efficiency of application of a decision support program in polyclinic practice during the covid-pandemic period." Meditsinskiy sovet = Medical Council, no. 4 (April 13, 2023): 77–85. http://dx.doi.org/10.21518/ms2023-016.

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Introduction. The issues of rational treatment of a new coronavirus infection (NCI), compliance of medical prescriptions with current clinical recommendations have been extremely relevant since the beginning of the pandemic. Of particular importance is the problem of overprescribing antimicrobials.Aim. To analyze the prescriptions of medicines and evaluate the results of the implementation of the medical decision support system (MDSS) among general practitioners and general practitioners of the Belgorod outpatient department in the treatment of NCIMaterials and methods. Treatment regimens for
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Yarushev, Sergei A., Alexey N. Averkin, Egor N. Volkov, and Andrey N. Lukyanov. "PROSPECTS OF APPLICATION OF EXPLANATORY ARTIFICIAL INTELLIGENCE IN MEDICAL DECISION SUPPORT SYSTEMS BASED ON ARTIFICIAL NEURAL NETWORKS." SOFT MEASUREMENTS AND COMPUTING 9, no. 70 (2023): 56–67. http://dx.doi.org/10.36871/2618-9976.2023.09.006.

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This paper explores the possibilities and prospects of applying explanatory artificial intelligence in medical decision support systems (MDSS) based on the use of artificial neural networks. A review of literature and sources is given. Conclusions are drawn about the benefits of using explanatory artificial intelligence in HISD by bridging the gap between system developers and endusers – clinicians.
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Rodríguez-González, Alejandro, Javier Torres-Niño, Miguel A. Mayer, Giner Alor-Hernandez, and Mark D. Wilkinson. "Analysis of a Multilevel Diagnosis Decision Support System and Its Implications: A Case Study." Computational and Mathematical Methods in Medicine 2012 (2012): 1–9. http://dx.doi.org/10.1155/2012/367345.

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Medical diagnosis can be performed in an automatic way with the use of computer-based systems or algorithms. Such systems are usually called diagnostic decision support systems (DDSSs) or medical diagnosis systems (MDSs). An evaluation of the performance of a DDSS called ML-DDSS has been performed in this paper. The methodology is based on clinical case resolution performed by physicians which is then used to evaluate the behavior of ML-DDSS. This methodology allows the calculation of values for several well-known metrics such as precision, recall, accuracy, specificity, and Matthews correlati
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Yarushev, Sergei A., Alexey N. Averkin, and Aleksandr O. Anurov. "DEVELOPMENT OF A MODULAR SOLUTION FOR SERVICE APPLICATIONS IN PERSONALIZED MEDICINE AND HEALTHCARE 5.0." SOFT MEASUREMENTS AND COMPUTING 6, no. 79 (2024): 68–78. http://dx.doi.org/10.36871/2618-9976.2024.06.007.

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The work explores the possibilities and prospects for the use of explanatory artificial intelligence in medical decision support systems (MDSS), based on the use of artificial neural networks. The study provides a review of literature and sources. Conclusions have been drawn about the benefits of using explanatory artificial intelligence in DSS by bridging the gap between system developers and end users – clinicians.
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Madadipouya, Kasra. "A Survey on Data Mining Algorithms and Techniques in Medicine." JOIV : International Journal on Informatics Visualization 1, no. 3 (2017): 61. http://dx.doi.org/10.30630/joiv.1.3.25.

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Medical Decision Support Systems (MDSS) industry collects a huge amount of data, which is not properly mined and not put to the optimum use. This data may contain valuable information that awaits extraction. The knowledge may be encapsulated in various patterns and regularities that may be hidden in the data. Such knowledge may prove to be priceless in future medical decision making. Available medical decision support systems are based on static data, which may be out of date. Thus, a medical decision support system that can learn the relationships between patient histories, diseases in the po
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Gavrilov, D. V., A. V. Kirilkina, and L. M. Serova. "Algorithm for forming a suspicion of a new coronavirus infection based on the analysis of symptoms for use in medical decision support systems." Vrach i informacionnye tehnologii, no. 4 (2020): 51–58. http://dx.doi.org/10.37690/1811-0193-2020-4-51-58.

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The course of the COVID‑19 pandemic imposes a significant burden on healthcare systems, including on primary care, when it is necessary to correctly suspect and determine further management. The symptoms non-specificity and the manifestations versatility of the COVID‑19 impose difficulties in identifying suspicions. To improve the definition of COVID‑19 symptom checkers and medical decision support systems (MDSS) can potentially be useful. They can give recommendations for determining the disease management. The scientific analysis shows the manifestations versatility and the occurrence freque
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Rozprawy doktorskie na temat "Medical Decision Support System (MDSS)"

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Kindsford, Dr Douglas. "A system of decision support for medical diagnosis." Thesis, University of Canterbury. Electrical Engineering, 2000. http://hdl.handle.net/10092/6761.

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This thesis presents DAMOCLES, a quantitative modelling approach to medical diagnosis that addresses several shortcomings of existing approaches to computer-assisted diagnosis. In developing this approach, several important epistemological issues are explored, and the nature of necessary anatomical, physiological, pathological and clinical knowledge in the medical domain is analysed in detail. A domain model architecture to represent this knowledge is proposed, the main contribution of which is an inductive method for determining from raw data the form of functions describing the relationships
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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 capabi
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Bindoff, IK. "An Intelligent Decision Support System for Automated Medication Review." Thesis, Honours thesis, University of Tasmania, 2005. https://eprints.utas.edu.au/251/1/Ivan_-_Thesis_Final.pdf.

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Knowledge Acquisition techniques are not historically designed for domains where the expert knowledge being modelled is inconsistent and poorly defined. This study explores the applicability of the MCRDR technique of knowledge acquisition towards a domain of this nature, medication reviews. Through this experimentation it is also sought to improve the quality of service that the medication reviewers can provide to their patients, by reducing the incidence of missed classifications. To facilitate this study a Knowledge-Based System was developed that employed the MCRDR method and which was c
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Vernier, Stanley J. "Clinical Evaluation and Enhancement of a Medical Case-Based Decision Support System." Ohio University / OhioLINK, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1258132492.

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Cihangir, Cigdem. "A Hierarchical Decision Support System For Workforce Planning In Medical Equipment Maintenance Services." Master's thesis, METU, 2010. http://etd.lib.metu.edu.tr/upload/12612778/index.pdf.

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In this thesis, we propose a hierarchical level decision support system for workforce planning in medical equipment maintenance services. In strategic level, customer clusters and the total number of field engineers is determined via a mixed integer programming and simulation. In MIP, we aim to find the minimum number of field engineers. Afterwards, we analyze service measures such as response time via simulation. In tactical level, quarterly training program for the field engineers is determined via mixed integer programming and the results are interpreted in terms of service level via simu
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Muza, Matej. "Development of a Spatial Decision Support System for Emergency Medical Service Facility Siting." Thesis, Virginia Tech, 2011. http://hdl.handle.net/10919/42583.

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Improved strategic location of an Emergency Medical Service (EMS) facility can significantly increase EMS efficiency. Urban planners need to consider a location that satisfies multiple criteria in order to make an informed decision about a future EMS facility site. Apart from basic criteria such as parcel value and size, decision-makers need to consider area and population coverage from potential parcels. Geographic Information Systems (GIS) provide an adequate analysis environment for EMS facility siting as many considered criteria are of a spatial nature. However, urban planners making decis
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Barton, Amy J. Gocsik. "Elements of a decision support system for chief nurse executives /." Licensed for access by UF Students, faculty, and staff (and others in a UF library), 1993. http://wwwlib.umi.com/cr/ufl/fullcit?p9607431.

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Treurnicht, Maria Jacoba. "A decision support system for telemedicine needs assessments in South Africa." Thesis, Stellenbosch : Stellenbosch University, 2012. http://hdl.handle.net/10019.1/20225.

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Thesis (MScEng)--Stellenbosch University, 2012.<br>ENGLISH ABSTRACT: The various applications of Information and Communication Technologies (ICTs) in healthcare are increasingly effective to improve the cost-effectiveness and quality of healthcare service delivery. Telemedicine is such an application, using ICTs to provide health services over a distance. Since 1997, the South African Department of Health has invested large amounts of capital to implement telemedicine systems in South Africa. Unfortunately, telemedicine programs have had little success since, leading to many workstations
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Olsson, Linda. "Possibilities for the development of a decision support system for diagnosing heart failure." Thesis, Linköping University, Department of Biomedical Engineering, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-9719.

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<p>Heart failure is a common disease which is difficult to diagnose. To aid physicians in diagnosing heart failure, a decision support system has been proposed. Parameters useful to the system are suggested. Some of these, such as age and gender, should be provided by the physician, and some should be derived from electro- and phonocardiographic signals.</p><p>Various methods of signal processing, such as wavelet theory and principal components analysis, are described. Heart failure should be diagnosed based on the parameters, and so various forms of decision support systems, such as neural ne
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Atyeo, Simon Vincent, and simon atyeo@defence gov au. "An Intelligent System for the Pre-Mission Analysis of Helicopter Emergency Medical Service Operations." RMIT University. Aerospace, Mechanical and Manufacturing Engineering, 2009. http://adt.lib.rmit.edu.au/adt/public/adt-VIT20090508.102128.

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The Helicopter Emergency Medical Service (HEMS) accident rate has driven operators from around the world to address the management of risks inherent to their operations. In-flight decision-making, pre-flight planning, failure to follow standard operating procedures, delayed remedial actions, and misinterpretation of environmental cues are all areas that need to be addressed for safe HEMS operations. HEMS operations are complex, being a joint exercise between the flight crew, paramedics and supporting agencies. Operations occur around-the-clock, in all-weather conditions, and often with no
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Książki na temat "Medical Decision Support System (MDSS)"

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Andrle, Stephen. Deploying the Maintenance Decision Support System (MDSS) in Iowa. Iowa State University, Center for Transportation Research and Education, 2003.

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Dyczkowski, Krzysztof. Intelligent Medical Decision Support System Based on Imperfect Information. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-67005-8.

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EATON, David J. Development and applications of a decision support system for emergency medical serices. University of Texas, 1988.

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Spackman, Kent Alan. Creating decision criteria from examples: The Criteria Learning System (CRLS). Spackman], 1988.

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International Conference on Systems Research, Informatics, and Cybernetics (19th 2007 Baden-Baden, Germany). Advances in environmental systems research: Sustainability, environmental sciences, support systems : effects of electromagnetic exposition on honeybees, principles of neuro-empirism and dynamic models, application of stochastic networks, sustainability of fuzzy theory, object oriented analysis, integrated logistic support principles, business information management system, sustainable decision support systems, health service delivery. International Institute for Advanced Studies in Systems Research and Cybernetics, 2007.

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International Conference on Systems Research, Informatics, and Cybernetics (19th 2007 Baden-Baden, Germany). Advances in environmental systems research: Sustainability, environmental sciences, support systems : effects of electromagnetic exposition on honeybees, principles of neuro-empirism and dynamic models, application of stochastic networks, sustainability of fuzzy theory, object oriented analysis, integrated logistic support principles, business information management system, sustainable decision support systems, health service delivery. International Institute for Advanced Studies in Systems Research and Cybernetics, 2007.

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L, Cats-Baril William, and Alemi Farrokh, eds. Systems to support health policy analysis: Theory, models, and uses. Health Administration Press, 1992.

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Dyczkowski, Krzysztof. Intelligent Medical Decision Support System Based on Imperfect Information: The Case of Ovarian Tumor Diagnosis. Springer, 2017.

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Dyczkowski, Krzysztof. Intelligent Medical Decision Support System Based on Imperfect Information: The Case of Ovarian Tumor Diagnosis. Springer, 2018.

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VA health care delivery: Top management leadership critical to success of decision support system : report to the Committee on Veterans' Affairs, House of Representatives. The Office, 1995.

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Części książek na temat "Medical Decision Support System (MDSS)"

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Seidl, Werner, and Thorsten Cypra. "Maintenance Decision Support System (MDSS) ASFINAG / Austria." In Advances in Intelligent and Soft Computing. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15503-1_13.

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Burstein, Frada, Pari Delir Haghighi, and Arkady Zaslavsky. "Context-Aware Mobile Medical Emergency Management Decision Support System for Safe Transportation." In Decision Support. Springer New York, 2010. http://dx.doi.org/10.1007/978-1-4419-6181-5_9.

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Fomin, Alexey, Mikhail Turov, Elena Matrosova, and Anna Tikhomirova. "Medical Knowledge-Based Decision Support System." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-63940-6_46.

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Tanaka, Hideo, Hisao Ishibuchi, and Takeo Shigenaga. "Fuzzy Inference System Based on Rough Sets and Its Application to Medical Diagnosis." In Intelligent Decision Support. Springer Netherlands, 1992. http://dx.doi.org/10.1007/978-94-015-7975-9_8.

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Rekha, K. S., R. Jeevananthan, G. Gowtham, and Prahalatha Raghavan V. Sai. "Medical Analytical Decision Support System Investigative Study." In Disruptive Technologies for Sustainable Development. CRC Press, 2024. http://dx.doi.org/10.1201/9781003428473-52.

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Redekop, W. Ken, and Donald W. Paty. "Development of a computerized neurological decision support system." In Medical Informatics Europe 1991. Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/978-3-642-93503-9_40.

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Kern, Josipa, Ante Bartolic, Silvije Vuletic, and Davor Ivankovi. "Decision Support System (DSS) in Managing Health Centers." In Medical Informatics Europe ’90. Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/978-3-642-51659-7_58.

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Nagy, L. L., B. Christensen, and T. J. Nagy. "An Expert System for Medical Evacuations." In Expert Systems and Decision Support in Medicine. Springer Berlin Heidelberg, 1988. http://dx.doi.org/10.1007/978-3-642-48706-4_41.

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Fernández del Pozo, J. A., C. Bielza, and M. Gómez. "Knowledge Organisation in a Neonatal Jaundice Decision Support System." In Medical Data Analysis. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-45497-7_13.

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Tache, Irina Andra, and Dumitru Popescu. "Medical Decision Support System for Lung Damage Classification." In Studies in Systems, Decision and Control. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-62158-1_8.

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Streszczenia konferencji na temat "Medical Decision Support System (MDSS)"

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Bhatti, Uzair Aslam, Guo Wenlong, Mughair Aslam Bhatti, et al. "ABi-LT: Intelligent Medical Decision Model Clinical Decision Support System." In 2025 2nd International Conference on Electronic Engineering and Information Systems (EEISS). IEEE, 2025. https://doi.org/10.1109/eeiss65394.2025.11085933.

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Sudhish, Dhanya K., Latha R. Nair, and Shailesh S. "Enhanced Content-Based Medical Image Retrieval in Healthcare Decision Support System Using AutoML and Vector Space Model." In 2024 IEEE Recent Advances in Intelligent Computational Systems (RAICS). IEEE, 2024. http://dx.doi.org/10.1109/raics61201.2024.10690076.

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Samaras, Agorastos-Dimitrios, Anna Feleki, Ioannis D. Apostolopoulos, et al. "Medical Decision Support System in Nuclear Medicine Diagnosis for Non-Small Cell Lung Cancer and Coronary Artery Disease: A First Stage Prototype." In 2024 15th International Conference on Information, Intelligence, Systems & Applications (IISA). IEEE, 2024. https://doi.org/10.1109/iisa62523.2024.10786612.

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Chandiok, Ashish, and D. K. Chaturvedi. "Cognitive Decision Support System for medical diagnosis." In 2016 International Conference on Computational Techniques in Information and Communication Technologies (ICCTICT). IEEE, 2016. http://dx.doi.org/10.1109/icctict.2016.7514604.

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AL.Hak, Layla A., Ismael S. Aref, Wasan Ahmed Ali, and Jumana Waleed. "Medical decision support framework using distribution system." In INTERNATIONAL RESEARCH CONFERENCE ON ENGINEERING AND APPLIED SCIENCES 2023: IRCEAS2023. AIP Publishing, 2025. https://doi.org/10.1063/5.0257581.

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Osman, Amira M., Walid I. Al-Atabany, Neven S. Saleh, and Ayman M. El-Deib. "Decision Support System for Medical Equipment Failure Analysis." In 2018 9th Cairo International Biomedical Engineering Conference (CIBEC). IEEE, 2018. http://dx.doi.org/10.1109/cibec.2018.8641786.

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Carvalho, Rebeck, Rahul Isola, and Amiya Kumar Tripathy. "MediQuery — An automated decision support system." In 2011 24th International Symposium on Computer-Based Medical Systems (CBMS). IEEE, 2011. http://dx.doi.org/10.1109/cbms.2011.5999050.

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Khattak, Asad Masood, Zeeshan Pervez, Manhyung Han, Chris Nugent, and Sungyoung Lee. "DDSS: Dynamic decision support system for elderly." In 2012 25th IEEE International Symposium on Computer-Based Medical Systems (CBMS). IEEE, 2012. http://dx.doi.org/10.1109/cbms.2012.6266346.

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Duarte de Almeida, Joao Rafael, and Jose Luis Oliveira. "GenericCDSS - A Generic Clinical Decision Support System." In 2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS). IEEE, 2019. http://dx.doi.org/10.1109/cbms.2019.00046.

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Tomakova, Rimma. "INTELLIGENT MEDICAL DECISION SUPPORT SYSTEM BASED ON INTERNET-TECHNOLOGY." In 16th International Multidisciplinary Scientific GeoConference SGEM2016. Stef92 Technology, 2016. http://dx.doi.org/10.5593/sgem2016/b21/s07.034.

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Raporty organizacyjne na temat "Medical Decision Support System (MDSS)"

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Brinster, Gregory L., Jairaj Desai, Myles W. Overall, et al. Evaluating the Robustness of MDSS Forecast and Compliance with Recommendations. Purdue University, 2025. https://doi.org/10.5703/1288284317805.

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The Indiana Department of Transportation (INDOT) adopted the Maintenance Decision Support System (MDSS) for user-defined plowing segments in the winter of 2008–2009. Since then, many new data sources, including connected vehicle data, enhanced weather data, and fleet telematics have been integrated into INDOT winter operations activities. The objective of this study was to use these new data sources to conduct a systematic evaluation of the robustness of the MDSS forecasts. During the 2023–2024 winter season, 26 unique MDSS forecast data attributes were collected at 0-, 1-, 3-, 6-, 12-, and 23
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Joyner, Michael J., and Betty Diamond. Preclinical Evaluation of a Decision Support Medical Monitoring System for Early Detection of Potential Hemodynamic Decompensation During Blood Loss in Humans. Defense Technical Information Center, 2012. http://dx.doi.org/10.21236/ada577046.

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Posacka, Anna, and Peter Ross. Tackling microfibre pollution through science, policy, and innovation: A framework for Canadian leadership. Raincoast Conservation Foundation, 2024. http://dx.doi.org/10.70766/47.9973.

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Microfibre pollution has emerged as a global conservation and public health concern, prompting a wave of research related to its source, transport, fate and effects, as well as mitigation strategies. This report provides support for the Government of Canada in decision-making on microfibres, by summarizing important elements of the topic from the scientific literature, stakeholder consultations and cross-sector activities. The goal herein is to characterize the primary sources, discharge mechanisms and environmental fate of microfibre pollution in Canada, review available methodologies and def
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