[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118426-en":3,"doc-seo-118426-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},118426,687197207057,"Sage","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Predicting Norwegian Elderly Hospitalizations using Machine Learning - Philosophiae Doctor Dissertation","Dissertation for a Philosophiae Doctor degree presenting approaches for predicting unplanned hospitalizations among older adults in Norway using machine learning. The work frames the burden created by an aging population, reviews challenges in distinguishing admission versus readmission, and situates prediction within the Norwegian admission process. Method sections describe data integration from national registries and related clinical sources, include a systematic review for Aim I, and develop modeling strategies that address high-dimensional health code systems using network analysis concepts.","Faculty of Health Sciences  \nPredicting Norwegian elderly hospitalizations using Machine Learning  \nMohsen Askar  \n| A dissertation for the degree of Philosophiae Doctor February 2025 |\n| --- |\n|  |\n\nA dissertation for the degree of Philosophiae Doctor  \nPredicting Norwegian elderly hospitalizations using  \nMachine Learning  \nMohsen Askar  \nFaculty of Health Sciences  \nDepartment of Pharmacy UiT – The Arctic University of Norway  \nTromsø, Norway 2025  \nFront page photo: the first page of Alan Turing's 1950 paper, \"Computing Machinery and Intelligence,\"published in MIND journal 49: 433-460. The paper introduces what is now known as the Turing Test, which explores the question: \"Can machines think?\". The paper is one of the foundational works for Artificial Intelligence and remains one of the most influential works in computing and cognitive science.  \nTable of Contents  \nScientific environment ..................................................................................................................... i  \nSummary ......................................................................................................................................... iii  \nAcknowledgement ........................................................................................................................... v  \nList of papers.................................................................................................................................. vii  \nList of other publications ............................................................................................................. viii  \nAbbreviations.................................................................................................................................. ix  \n1 Background............................................................................................................................... 1  \n1.1 Hospitalizations ............................................................................................................... 1  \n1.1.1 Aging population and burden of hospitalizations...................................................... 1  \n1.1.2 Why predicting unplanned hospitalization is difficult? ........................................... 2  \n1.1.3 Hospital admission or readmission? ...........................................................................3  \n1.1.4 The Norwegian healthcare system and process of admission ...................................3  \n1.1.5 Approaches to release the hospitalization burden.....................................................3  \n1.1.6 Methods for predicting hospital admissions ............................................................. 4  \n1.2 Machine Learning ........................................................................................................... 4  \n1.2.1 A brief insight into Machine Learning history .......................................................... 4  \n1.2.2 ML in healthcare ......................................................................................................... 5  \n1.2.3 Drivers of current ML adoption in healthcare .......................................................... 7  \n1.2.4 Epidemiology, Statistics, or Machine Learning? ....................................................... 8  \n1.2.5 General challenges on the way ................................................................................... 8  \n1.2.6 A specific challenge: Handling high-dimensional Health Code Systems (HCSs) in ML models ............................................................................................................................... 9  \n1.3 Network Analysis (NA) ................................................................................................... 9  \n1.3.1 Briefly on NA history in healthcare............................................................................ 9  \n1.3.2 Multimorbidity networks ..........................................","cbCaicAU0XhVisko","https://ap.wps.com/l/cbCaicAU0XhVisko","pdf",10575859,1,216,"English","en",105,"# Summary\n# Acknowledgement\n# 1 Background\n## Hospitalizations\n## Machine Learning\n## Network Analysis (NA)\n# 2 Aims\n# 3 Materials and Methods\n## Data collection and sources\n## Study design, population, and settings\n## Systematic review of prediction using ML (Aim I)\n## Representing Health code systems in models (Aim II)","[{\"question\":\"What problem does the dissertation address?\",\"answer\":\"It addresses predicting unplanned hospitalizations among Norwegian elderly patients, motivated by the increasing burden from an aging population and the difficulty of accurate prediction.\"},{\"question\":\"What data sources are used for the study?\",\"answer\":\"The dissertation integrates data from multiple registries and clinical datasets, including MIMIC-III and Norwegian national registries such as the National Population Registry, the Norwegian Patient Registry, and prescription and municipal registries.\"},{\"question\":\"How does the dissertation treat high-dimensional health code systems in machine learning models?\",\"answer\":\"It devotes a specific aim to representing high-dimensional health code systems within models, supported by an experimental design and modeling strategy and framed within broader methodological challenges.\"}]","Predicting Norwegian Elderly Hospitalizations using Machine Learning - 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