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Dissertations / Theses on the topic 'Predictive healthcare'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

Wickramasuriya, Dilranjan S. "Predictive Analytics in Cardiac Healthcare and 5G Cellular Networks." Scholar Commons, 2017. http://scholarcommons.usf.edu/etd/6980.

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This thesis proposes the use of Machine Learning (ML) to two very distinct, yet compelling, applications – predicting cardiac arrhythmia episodes and predicting base station association in 5G networks comprising of virtual cells. In the first scenario, Support Vector Machines (SVMs) are used to classify features extracted from electrocardiogram (EKG) signals. The second problem requires a different formulation departing from traditional ML classification where the objective is to partition feature space into constituent class regions. Instead, the intention here is to identify temporal pattern

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Victors, Mason Lemoyne. "A Classification Tool for Predictive Data Analysis in Healthcare." BYU ScholarsArchive, 2013. https://scholarsarchive.byu.edu/etd/5639.

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Hidden Markov Models (HMMs) have seen widespread use in a variety of applications ranging from speech recognition to gene prediction. While developed over forty years ago, they remain a standard tool for sequential data analysis. More recently, Latent Dirichlet Allocation (LDA) was developed and soon gained widespread popularity as a powerful topic analysis tool for text corpora. We thoroughly develop LDA and a generalization of HMMs and demonstrate the conjunctive use of both methods in predictive data analysis for health care problems. While these two tools (LDA and HMM) have been used in co

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Gligorijevic, Djordje. "Predictive Uncertainty Quantification and Explainable Machine Learning in Healthcare." Diss., Temple University Libraries, 2018. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/520057.

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Computer and Information Science<br>Ph.D.<br>Predictive modeling is an ever-increasingly important part of decision making. The advances in Machine Learning predictive modeling have spread across many domains bringing significant improvements in performance and providing unique opportunities for novel discoveries. A notably important domains of the human world are medical and healthcare domains, which take care of peoples' wellbeing. And while being one of the most developed areas of science with active research, there are many ways they can be improved. In particular, novel tools developed ba

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Daffue, Ruan Albert. "Applying patient-admission predictive algorithms in the South African healthcare system." Thesis, Stellenbosch : Stellenbosch University, 2013. http://hdl.handle.net/10019.1/79897.

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Thesis (MScEng)--Stellenbosch University, 2013.<br>ENGLISH ABSTRACT: Predictive analytics in healthcare has become one of the major focus areas in healthcare delivery worldwide. Due to the massive amount of healthcare data being captured, healthcare providers and health insurers are investing in predictive analytics and its enabling technologies to provide valuable insight into a large variety of healthcare outcomes. One of the latest developments in the field of healthcare predictive modelling (PM) was the launch of the Heritage Health Prize; a competition that challenges individuals from acr

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Tekieh, Mohammad Hossein. "Analysis of Healthcare Coverage Using Data Mining Techniques." Thèse, Université d'Ottawa / University of Ottawa, 2012. http://hdl.handle.net/10393/20547.

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This study explores healthcare coverage disparity using a quantitative analysis on a large dataset from the United States. One of the objectives is to build supervised models including decision tree and neural network to study the efficient factors in healthcare coverage. We also discover groups of people with health coverage problems and inconsistencies by employing unsupervised modeling including K-Means clustering algorithm. Our modeling is based on the dataset retrieved from Medical Expenditure Panel Survey with 98,175 records in the original dataset. After pre-processing the data, includi

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Caglar, Toros. "A Queueing Theoretic Approach to Gridlock Prediction in Emergency Departments." Thesis, Virginia Tech, 2005. http://hdl.handle.net/10919/34556.

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When an emergency department (ED) decides that it is not going to be able to serve any more newly arriving patients, it declares "diversion". When an ED is on diversion, it suspends arrivals that can be controlled by forcing some or all of the incoming emergency medical system (EMS) transport units to search for alternate treatment facilities for their patients. This search causes both patients and EMS crew to loose valuable time. Contrary to the general belief that suggests diversions are not very common, the results of the American Hospital Association survey present an example where one

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Espinoza, Sofia Elizabeth. "Data mining methods applied to healthcare problems." Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/44903.

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Growing adoption of health information technologies is allowing healthcare providers to capture and store enormous amounts of patient data. In order to effectively use this data to improve healthcare outcomes and processes, clinicians need to identify the relevant measures and apply the correct analysis methods for the type of data at hand. In this dissertation, we present various data mining and statistical methods that could be applied to the type of datasets that are found in healthcare research. We discuss the process of identification of appropriate measures and statistical tools, the ana

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Lin, Yu-Kai, Hsinchun Chen, Randall A. Brown, Shu-Hsing Li, and Hung-Jen Yang. "HEALTHCARE PREDICTIVE ANALYTICS FOR RISK PROFILING IN CHRONIC CARE: A BAYESIAN MULTITASK LEARNING APPROACH." SOC INFORM MANAGE-MIS RES CENT, 2017. http://hdl.handle.net/10150/625248.

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Clinical intelligence about a patient's risk of future adverse health events can support clinical decision making in personalized and preventive care. Healthcare predictive analytics using electronic health records offers a promising direction to address the challenging tasks of risk profiling. Patients with chronic diseases often face risks of not just one, but an array of adverse health events. However, existing risk models typically focus on one specific event and do not predict multiple outcomes. To attain enhanced risk profiling, we adopt the design science paradigm and propose a principl

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Jiao, Weiwei. "Predictive Analysis for Trauma Patient Readmission Database." The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1492718909631318.

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Cheng, Chih-Wen. "Development of integrated informatics analytics for improved evidence-based, personalized, and predictive health." Diss., Georgia Institute of Technology, 2015. http://hdl.handle.net/1853/54872.

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Advanced information technologies promise a massive influx of individual-specific medical data. These rich sources offer great potential for an increased understanding of disease mechanisms and for providing evidence-based and personalized clinical decision support. However, the size, complexity, and biases of the data pose new challenges, which make it difficult to transform the data to useful and actionable knowledge using conventional statistical analysis. The so-called “Big Data” era has created an emerging and urgent need for scalable, computer-based data mining methods that can turn data

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Espinal, Redondez Luis Ángel, Alvarado Cinthia Mónica Ibáñez, and Melo Manuel Alejandro Javier Armando Moyano. "Propuesta de un Modelo Predictivo para Realizar un Control y Supervisión más Eficiente de las Prestaciones de Servicios de Salud en una Aseguradora Pública de Salud." Master's thesis, Universidad Peruana de Ciencias Aplicadas (UPC), 2020. http://hdl.handle.net/10757/652194.

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El acceso a un sistema de salud digno constituye uno de los derechos fundamentales de toda persona, en el Perú se han realizado grandes esfuerzos para mejorar la calidad de los sistemas de salud, es un desafío al bicentenario el brindar un aseguramiento de salud de calidad que pueda alcanzar a todos los peruanos. Este objetivo enfrenta grandes desafíos ya que existen deficiencias en los procesos de las instituciones que brindan servicios de salud, siendo la Aseguradora Pública de Salud uno de los principales actores en el aseguramiento de la salud en el Perú. Nuestra investigación se

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Kabir, Md Faisal. "Extracting Useful Information and Building Predictive Models from Medical and Health-Care Data Using Machine Learning Techniques." Diss., North Dakota State University, 2020. https://hdl.handle.net/10365/31924.

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In healthcare, a large number of medical data has emerged. To effectively use these data to improve healthcare outcomes, clinicians need to identify the relevant measures and apply the correct analysis methods for the type of data at hand. In this dissertation, we present various machine learning (ML) and data mining (DM) methods that could be applied to the type of data sets that are available in the healthcare area. The first part of the dissertation investigates DM methods on healthcare or medical data to find significant information in the form of rules. Class association rule mining, a v

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Hensley, Vicki. "CHIILDHOOD BULLYING: ASSESSMENT PRACTICES AND PREDICTIVE FACTORS ASSOCIATED WITH ASSESSING FOR BULLYING BY HEALTH CARE PROVIDERS." UKnowledge, 2015. http://uknowledge.uky.edu/nursing_etds/25.

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Childhood bullying affects over 25% of today’s youth and causes up to 160,000 missed school days per year. Bullying causes short and long term adverse effects to both mental and physical health. Many organizations encourage healthcare providers to take an active role in bullying prevention. However, there has been little research into the role of primary healthcare providers regarding childhood bullying and the effectiveness of different approaches to screening and management. Therefore the purposes of this dissertation were to a) explore childhood bullying and the role of the healthcare provi

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Fouayzi, Hassan. "Using Healthcare Data to Inform Health Policy: Quantifying Cardiovascular Disease Risk and Assessing 30-Day Readmission Measures." eScholarship@UMMS, 2019. https://escholarship.umassmed.edu/gsbs_diss/1031.

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Health policy makers are struggling to manage health care and spending. To identify strategies for improving health quality and reducing health spending, policy makers need to first understand health risks and outcomes. Despite lacking some desirable clinical detail, existing health care databases, such as national health surveys and claims and enrollment data for insured populations, are often rich in information relating patient characteristics to heath risks and outcomes. They typically encompass more inclusive populations than can feasibly be achieved with new data collection and are valua

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Corné, Josefine, and Amanda Ullvin. "Prediktiv analys i vården : Hur kan maskininlärningstekniker användas för att prognostisera vårdflöden?" Thesis, KTH, Skolan för teknik och hälsa (STH), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-211286.

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Projektet genomfördes i samarbete med Siemens Healthineers i syfte att utreda möjligheter till att prognostisera vårdflöden. Det genom att undersöka hur big data tillsammans med maskininlärning kan utnyttjas för prediktiv analys. Projektet utgjordes av två fallstudier med mål att, baserat på data från tidigare MRT-undersökningar, förutspå undersökningstider för kommande undersökningar respektive identifiera patienter som riskerar att missa inbokad undersökning. Fallstudierna utfördes med hjälp av programmeringsspråket R och tre olika inbyggda funktioner för maskininlärning användes för att ta

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Inoriza, José María. "Morbilidad, utilización de recursos y costes sanitarios en la comarca del Baix Empordà." Doctoral thesis, Universitat de Girona, 2015. http://hdl.handle.net/10803/301435.

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Los sistemas de atención sanitaria tienden en la actualidad a migrar de formas de organización fragmentadas por líneas o niveles, que al mismo tiempo compiten e intentan coordinarse entre sí, hacia formas que integran todos los ámbitos de la oferta asistencial bajo una misma organización, sea virtual o real. Se denominan habitualmente organizaciones sanitarias integradas. La presente tesis incluye un conjunto de estudios que trata de analizar las necesidades asistenciales (morbilidad atendida) de la población de una comarca; comprender la utilización de recursos asistenciales realizada, inclu

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Jenner, Elizabeth Anne. "Healthcare professionals' hand hygiene : predicting and improving practice." Thesis, University of Hertfordshire, 2005. http://hdl.handle.net/2299/14270.

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This programme of research consists of eight studies which sought to determine how healthcare professionals' hand hygiene practice might be improved. The Theory of Planned Behaviour was used to isolate perceived cognitive and physical factors that may explain the variance in their hand hygiene behaviour. Practice was observed and healthcare professionals' understanding of the hand hygiene policy to which they were expected to adhere was assessed. Messages on hand hygiene posters were analysed. The effect of two educational interventions on students' attitudes was tested. Achieving change will

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Kupera, Geoffrey Takura. "Predicting healthcare practitioners' research use : application of theory pf planned behaviour." Thesis, Manchester Metropolitan University, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.274246.

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Damberg, Emmy. "Data Mining for Description and Prediction of Antibiotic Treated Healthcare-Associated Infections." Thesis, KTH, Skolan för teknik och hälsa (STH), 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-154200.

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Healthcare-associated infections is the most common healthcare related injury and affect almost every tenth patient. With the purpose of reducing these infections Infektionsverktyget, The Anti-Infection Tool, was developed for registration and feedback of infection data. The tool is now used in all Swedish county councils resulting in a wealth of data. The purpose of this thesis was thus to investigate how data mining can be applied to describe patterns in this data and predict patient outcomes regarding healthcare-associated infections that need to be treated with antibiotics. Data mining was

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Ghosh, Arindam. "Personal Healthcare Agents for Monitoring and Predicting Stress and Hypertension from Biosignals." Doctoral thesis, Università degli studi di Trento, 2016. https://hdl.handle.net/11572/367585.

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We live in exciting times. The fast paced growth in mobile computers has put powerful computational devices in the palm of our hands. Blazing fast connectivity has made human-human, human-machine, and machine-machine communication effortless. Wearable devices and the internet of things have made monitoring every aspect of our lives easier. This has given rise to the domain of quantified self where we can continuous record and quantify the various signals generated in everyday life. Sensors on smartphones can continuously record our location and motion profile. Sensors on wearable devices can t

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Ghosh, Arindam. "Personal Healthcare Agents for Monitoring and Predicting Stress and Hypertension from Biosignals." Doctoral thesis, University of Trento, 2016. http://eprints-phd.biblio.unitn.it/1914/1/PhD-Thesis-Arindam-Ghosh.pdf.

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We live in exciting times. The fast paced growth in mobile computers has put powerful computational devices in the palm of our hands. Blazing fast connectivity has made human-human, human-machine, and machine-machine communication effortless. Wearable devices and the internet of things have made monitoring every aspect of our lives easier. This has given rise to the domain of quantified self where we can continuous record and quantify the various signals generated in everyday life. Sensors on smartphones can continuously record our location and motion profile. Sensors on wearable devices can

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Hernandez, Sarah E. "PREDICTING BURNOUT AMONG PSYCHIATRIC TECHNICIANS." CSUSB ScholarWorks, 2016. https://scholarworks.lib.csusb.edu/etd/304.

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This thesis attempted to examine stressors associated with the psychiatric technician profession and levels of burnout amongst them. This was accomplished through the use of a survey which included demographic data, the Maslach Burnout Inventory Human Services Survey (MBI‑HSS), an adaptation of the Nursing Stress Scale (NSS) and a self‑care assessment tool. A total of three research questions were examined statistically including levels of burnout on subcategories (personal accomplishment, emotional exhaustion and depersonalization), most common stressors identified by psychiatric technicians

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Phillips, Trenton J. "Predicting Length of Service for Nurses: An Analysis of a Healthcare Organization’s Selection Inventory." Xavier University / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=xavier1565860881878251.

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Froemke, Cecily Corrine. "Enhancing Value-Based Healthcare with Reconstructability Analysis: Predicting Risk for Hip and Knee Replacements." PDXScholar, 2017. https://pdxscholar.library.pdx.edu/open_access_etds/3772.

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Legislative reforms aimed at slowing growth of US healthcare costs are focused on achieving greater value, defined specifically as health outcomes achieved per dollar spent. To increase value while payments are diminishing and tied to individual outcomes, healthcare must improve at predicting risks and outcomes. One way to improve predictions is through better modeling methods. Current models are predominantly based on logistic regression (LR). This project applied Reconstructability Analysis (RA) to data on hip and knee replacement surgery, and considered whether RA could create useful models

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Van, der Haas Yvette Jane. "Predicting Myocardial Infarction using Textual Prehospital Data and Machine Learning." Thesis, Luleå tekniska universitet, Datavetenskap, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-84925.

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A major healthcare problem is the overcrowding of hospitals and emergency departments which leads to negative patient outcomes and increased costs. In a previous study, performed by Leiden University Medical Centre, a new and innovative prehospital triage method was developed where two nurse paramedics could consult a cardiologist for patients with cardiac symptoms, via a live connection on a digital triage platform. The developed triage method resulted in a recall = 0.995 and specificity = 0.0113. This study arise the following research question: ‘Would there be enough (good) information gath

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Laczik, Tamás. "Encoding Temporal Healthcare Data for Machine Learning." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-299433.

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This thesis contains a review of previous work in the fields of encoding sequential healthcare data and predicting graft- versus- host disease, a medical condition, based on patient history using machine learning. A new encoding of such data is proposed for machine learning purposes. The proposed encoding, called bag of binned weighted events, is a combination of two strategies proposed in previous work, called bag of binned events and bag of weighted events. An empirical experiment is designed to evaluate the predictive performance of the proposed encoding over various binning windows to that

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Ducey, Adam J. "Predicting Tablet Computer Use: An Extended Technology Acceptance Model." Scholar Commons, 2013. http://scholarcommons.usf.edu/etd/4471.

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While information technology has rapidly changed work in the United States in the past 50 years, some businesses and industries have been slow to adopt new technologies. Healthcare is one industry that has lagged behind in information technology investment for a variety of reasons. Recent federal initiatives to encourage IT adoption in the healthcare industry provide an ideal context to study factors that influence technology acceptance. Data from 261 practicing pediatricians were collected to evaluate an extended Technology Acceptance Model. Results indicated that individual (i.e., perceived

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Hamesse, Charles. "Simultaneous Measurement Imputation and Rehabilitation Outcome Prediction for Achilles Tendon Rupture." Thesis, KTH, Robotik, perception och lärande, RPL, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-231485.

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Achilles tendonbrott (Achilles Tendon Rupture, ATR) är en av de typiska mjukvävnadsskadorna. Rehabilitering efter sådana muskuloskeletala skador förblir en långvarig process med ett mycket variet resultat. Att kunna förutsäga rehabiliteringsresultat exakt är avgörande för beslutsfattande stöduppdrag. I detta arbete designar vi en probabilistisk modell för att förutse rehabiliteringsresultat för ATR med hjälp av en klinisk kohort med många saknade poster. Vår modell är tränad från början till slutet för att samtidigt förutsäga de saknade inmatningarna och rehabiliteringsresultat. Vi utvärderar

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Steinberg, Tara Celia. "Psychosocial factors predicting health status and perceived effectiveness of healthcare in an AIDS/HIV population." Ann Arbor, Mich. : ProQuest, 2006. 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:1434190.

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Thesis (M.A. in Clinical/Counseling Psychology)--S.M.U.<br>Title from PDF title page (viewed July 17, 2007). Source: Masters Abstracts International, Volume: 44-05, page: 2450. Adviser: Ephrem Fernandez. Includes bibliographical references.

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Plewa, Luke Joseph. "Sudden Cardiac Arrest Prediction through Heart Rate Variability Analysis." DigitalCommons@CalPoly, 2015. https://digitalcommons.calpoly.edu/theses/1449.

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The increase in popularity for wearable technologies (see: Apple Watch and Microsoft Band) has opened the door for an Internet of Things solution to healthcare. One of the most prevalent healthcare problems today is the poor survival rate of out-of hospital sudden cardiac arrests (9.5% on 360,000 cases in the USA in 2013). It has been proven that heart rate derived features can give an early indicator of sudden cardiac arrest, and that providing an early warning has the potential to save many lives. Many of these new wearable devices are capable of providing this warning through their heart ra

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Sasseville, Maxime. "Description des facteurs prédictifs de résultats d’une intervention de prévention et de gestion des maladies chroniques en contexte de soins première ligne." Mémoire, Université de Sherbrooke, 2014. http://hdl.handle.net/11143/6018.

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Résumé : Objectif : Identifier les facteurs associés avec le succès d’une intervention multidisciplinaire de prise en charge et de prévention des maladies chroniques dans un contexte de soins de santé de première ligne. Devis : Étude corrélationnelle prédictive d’analyse secondaire des données du projet PR1MaC, un essai randomisé contrôlé analysant les effets d’une intervention intégrant un programme de prise en charge et de prévention Contexte : Huit cliniques de soins de première ligne de la région Saguenay-Lac-Saint-Jean. Participants : un échantillon de 160 patients (52,5% d’hommes) réfé

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Molnar, Amber. "Predicting Graft Loss Following Acute Kidney Injury in Patients With a Kidney Transplant." Thesis, Université d'Ottawa / University of Ottawa, 2016. http://hdl.handle.net/10393/34236.

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Acute kidney injury (AKI), characterized by an abrupt loss of kidney function with retention of nitrogenous waste products, is common in the months to years following kidney transplantation and is associated with an increased risk of transplant failure (graft loss). Kidney transplant patients who experience graft loss and return to dialysis have an increased mortality risk and a lower quality of life. Research involving kidney transplant patients can prove challenging, as they are relatively small in number. To increase statistical power, researchers may utilize administrative databases. Howev

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Frandsen, Abraham Jacob. "Machine Learning for Disease Prediction." BYU ScholarsArchive, 2016. https://scholarsarchive.byu.edu/etd/5975.

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Millions of people in the United States alone suffer from undiagnosed or late-diagnosed chronic diseases such as Chronic Kidney Disease and Type II Diabetes. Catching these diseases earlier facilitates preventive healthcare interventions, which in turn can lead to tremendous cost savings and improved health outcomes. We develop algorithms for predicting disease occurrence by drawing from ideas and techniques in the field of machine learning. We explore standard classification methods such as logistic regression and random forest, as well as more sophisticated sequence models, including recurre

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Smith, Tristan. "Predicting Emergency Room Readmission Rates Using Data Mining Techniques." Bowling Green State University / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1587485194751687.

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Hellstenius, Sasha. "Model comparison of patient volume prediction in digital health care." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-229908.

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Accurate predictions of patient volume are an essential tool to improve resource allocation and doctor utilization in the traditional, as well as the digital health care domain. Varying methods for patient volume prediction within the traditional health care domain has been studied in contemporary research, while the concept remains underexplored within the digital health care domain. In this paper, an evaluation of how two different non-linear state-of-the-art time series prediction models compare when predicting patient volume within the digital health care domain is presented. The models co

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Shin, Jung-Ho. "New outcome-specific comorbidity scores excelled in predicting in-hospital mortality and healthcare charges in administrative databases." Doctoral thesis, Kyoto University, 2021. http://hdl.handle.net/2433/263579.

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Khan, Mohammad Ariful Alam. "Predicting the Risk of Chronic Disease: A Framework Based on Graph Theory and Social Network Analysis." Thesis, The University of Sydney, 2016. http://hdl.handle.net/2123/16557.

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Chronic diseases progress slowly and the patients are often first diagnosed with the disease when admitted to the hospital, resulting in further complications and an increased burden on limited healthcare resources. This thesis focuses on to understand the chronic disease progression and then predict the risk of the disease, subsequently preventing hospital admissions. To do that, the thesis presents a framework that has two major parts: (i) to understand and represent the progression of a particular chronic disease, and (ii) to develop a model based on that understanding to predict chronic di

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Leelahavarong, Pattara. "Development of an alcohol intervention model for predicting healthcare costs, life years, quality-adjusted life years and using for economic evaluation." Thesis, University of Glasgow, 2018. http://theses.gla.ac.uk/30672/.

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Objectives To develop an alcohol intervention model that predicts life years (LYs), quality adjusted life years (QALYs), and healthcare costs classified by the Alcohol Use Disorder Identification Test (AUDIT) screening tool and other various risk factors related to alcohol consumption. Furthermore, the developed model was transferred to the Thai setting. Methods Eight Scottish Health Surveys from 1995-2012 were linked to Scottish morbidity records and death records for the period 1981 to the end of 2013. Parametric survival analysis was used to estimate the hazard risks of first alcohol-relate

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Albertson, Justin Paul. "Development and validation of a prediction rule for methicillin-resistant Staphylococcus aureus recurrent infection among a veterans affairs healthcare system population." Thesis, University of Iowa, 2014. https://ir.uiowa.edu/etd/4561.

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Objective: Recurrent methicillin-resistant Staphylococcus aureus (MRSA) infections are a significant problem in the healthcare system. Our objective was to create a clinical prediction rule to identify Veterans at high-risk of recurrent MRSA infections. Methods: A retrospective cohort study of Veterans with MRSA bacteremia was performed using patient data from 2003 to 2011. Recurrent MRSA infection was defined as a positive blood culture between two days and 180 days after discharge from the index hospitalization. Severity of illness was measured at the time of admission using a modified APACH

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McCullen, Jeffrey Reynolds. "Predicting the Effects of Sedative Infusion on Acute Traumatic Brain Injury Patients." Thesis, Virginia Tech, 2020. http://hdl.handle.net/10919/105140.

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Healthcare analytics has traditionally relied upon linear and logistic regression models to address clinical research questions mostly because they produce highly interpretable results [1, 2]. These results contain valuable statistics such as p-values, coefficients, and odds ratios that provide healthcare professionals with knowledge about the significance of each covariate and exposure for predicting the outcome of interest [1]. Thus, they are often favored over new deep learning models that are generally more accurate but less interpretable and scalable. However, the statistical power of lin

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Curry, Cheryl J. "Predicting the Effects of Extrinsic and Intrinsic Job Factors on Overall Job Satisfaction for Generation X and Baby Boomers in a Regional Healthcare Organization." Digital Archive @ GSU, 2005. http://digitalarchive.gsu.edu/pmap_diss/27.

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This dissertation investigates the impact of intrinsic and extrinsic job factors on overall employee job satisfaction for two generation cohort groups, Baby Boomers and Generation X, in a small rural healthcare organization. Eight job factors were selected for the study, reflecting popular characteristics associated with the two groups. The job factors were classified as intrinsic or extrinsic using Hertzberg’s two-factor theory. Intrinsic factors studied were: work itself, promotion, and recognition. Extrinsic factors studied were: pay, supervision, people, technology, and work-family balance

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Ballester, Nicholas A. "Engineering Inpatient Discharges: Disposition Prediction and Day-of-Discharge Planning." Wright State University / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=wright1509728298874385.

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Medling, Nicholas. "The Individual Mandate, Commerce Clause, and Supreme Court: Predicting the Court's Ruling in HHS v. Florida." Scholarship @ Claremont, 2012. http://scholarship.claremont.edu/cmc_theses/345.

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An analysis of the evolution of the Commerce Clause, the Justices on the Supreme Court, and the arguments presented in this case indicate that the minimum coverage provision of the Patient Protection and Affordable Care Act will be struck down. Although the Court will likely be split 5 to 4 along ideological lines, each of the justices will have a unique rationale behind their decision. Chief Justice Roberts, Justice Scalia, and Justice Kennedy were heavily targeted by both parties’ oral and written arguments because there was speculation that any one of these traditionally conservative justic

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Toofanee, Mohammud Shaad Ally. "An innovative ecosystem based on deep learning : Contributions for the prevention and prediction of diabetes complications." Electronic Thesis or Diss., Limoges, 2023. https://aurore.unilim.fr/theses/nxfile/default/656b0a1f-2ff2-49c5-bb3e-f34704d6f6b0/blobholder:0/2023LIMO0107.pdf.

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En 2021, le diabète touchait environ 537 millions de personnes dans le monde. Ce chiffre devrait grimper à 643 millions d'ici 2030 et 783 millions d'ici 2045. Le diabète est une maladie métabolique persistante qui nécessite des soins et une gestion quotidiens continus. Le fardeau des maladies chroniques pèse lourdement sur les systèmes de santé lorsqu'il touche une partie substantielle de la population. De telles circonstances ont un impact négatif non seulement sur le bien-être général d'une grande partie de la population, mais contribuent également de manière significative aux dépenses de sa

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Pinaire, Jessica. "Explorer les trajectoires de patients via les bases médico-économiques : application à l'infarctus du myocarde." Thesis, Montpellier, 2017. http://www.theses.fr/2017MONTS020/document.

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Avec environ 120 000 personnes atteintes chaque année, 12 000 décès suite à la première crise et 18 000 décès après une année, l'infarctus du myocarde est un enjeu majeur de santé publique. Cette pathologie nécessite une hospitalisation et une prise en charge dans une unité de soins intensifs de cardiologie. Pour étudier cette pathologie, nous nous sommes orientés vers les bases hospitalières du PMSI.La collecte des données hospitalières dans le cadre du PMSI génère sur le plan national des bases de données de l'ordre de 25 millions d'enregistrements par an.Ces données, qui sont initialement r

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Gonçalves, Filipe da Silva. "Predictive analysis in healthcare." Master's thesis, 2018. http://hdl.handle.net/10071/17295.

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The Emergency departments (ED) are the major entry point to the healthcare system. With the growing demand due to the increase of life expectancy and the greater number of diseases, it is mandatory for the ED’s to have a more efficient resource management in order to try and provide the best experience possible to its patients. If the resource demand is greater than the resources available, then ED crowding occurs. This phenomenon leads to several problems that affect the patient experience, like longer waiting times, lack of beds, patients in hallways, etc. One of the ways to improve p

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Pantaleão, Bernardo Neves. "Predicting healthcare high-cost users using data mining methods." Master's thesis, 2022. http://hdl.handle.net/10362/134138.

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Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business Intelligence<br>The increase in healthcare costs is, perhaps, one of the most important issues that governments and organizations face nowadays. An ageing population and technological advancements are the key reasons for this phenomenon. In this scenario, proactive measures are very important. This work aimed to improve the effectiveness of the prevention by helping the identification of the most probable high-cost users of health

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Chantamit-o-pas, Pattanapong. "A Predictive Analysis of Electronic Healthcare Records for Stroke Symptoms." Thesis, 2019. http://hdl.handle.net/10453/140166.

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University of Technology Sydney. Faculty of Engineering and Information Technology.<br>Cerebrovascular symptoms, commonly known as stroke, can affect different parts of the human body depending on the area of brain affected. The patients who survive usually have a poor quality of life because of serious illness, long-term disability and become a burden to their families and the health care system. There is a strong demand for the management focused on prevention and early treatment of disease by analysing different factors. However, a high volume of medical data, heterogeneity, and complexity

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Fernandes, Maria Inês Resende da Lomba. "Data Mining Application for Healthcare Sector: Predictive Analysis of Heart Attacks." Master's thesis, 2021. http://hdl.handle.net/10362/127475.

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Project Work presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business Intelligence<br>Cardiovascular diseases are the main cause of the number of deaths in the world, being the heart disease the most killing one affecting more than 75% of individuals living in countries of low and middle earnings. Considering all the consequences, firstly for the individual’s health, but also for the health system and the cost of healthcare (for instance, treatments and medication), specifically for cardiovascular di

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Zhang, Saijuan. "Bayesian Methods in Nutrition Epidemiology and Regression-based Predictive Models in Healthcare." Thesis, 2010. http://hdl.handle.net/1969.1/ETD-TAMU-2010-12-8776.

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This dissertation has mainly two parts. In the first part, we propose a bivariate nonlinear multivariate measurement error model to understand the distribution of dietary intake and extend it to a multivariate model to capture dietary patterns in nutrition epidemiology. In the second part, we propose regression-based predictive models to accurately predict surgery duration in healthcare. Understanding the distribution of episodically consumed dietary components is an important problem in public health. Short-term measurements of episodically consumed dietary components are zero-inflated skewe

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