[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120016-en":3,"doc-seo-120016-105":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},120016,4398048950312,"Violet","https://ap-avatar.wpscdn.com/avatar/400002538284de19e3c?_k=1778320343897328908",8,"Research & Report","From Limited Data to Meaningful Insights - Two studies on chronic stress prediction using machine learning - Doctoral thesis in medicine (PhD)","This doctoral thesis evaluates how machine learning can enable chronic stress prediction when data are limited, emphasizing sample-size, data quality, and interpretability. Two studies examine chronic stress measurement and modeling in distinct populations, using multiple algorithms in Study 1 and XGBoost with SHAP-based feature explanations in Study 2. The work analyzes predictive performance, variable rankings, and methodological constraints of small datasets, concluding with implications, strengths, and limitations for future applications in healthcare research.","From Limited Data to Meaningful Insights  \nTwo studies on chronic stress prediction using machine learning  \nDoctoral thesis to obtain a doctorate (PhD) from the Faculty of Medicine of the University of Bonn  \nArezoo Bozorgmehr  \nfrom Shahreza, Iran  \n2024  \nWritten with authorization of  \nthe Faculty of Medicine of the University of Bonn  \nFirst reviewer: [Prof. Dr. med. Birgitta Weltermann MPH](Prof. Dr. med. Birgitta Weltermann MPH)(USA)  \nSecond reviewer: Dr. rer. nat. Javad Ghofrani, University of Luebeck (Germany)  \nDay of oral examination: 06/11/2023  \nFrom the Institute of General Practice and Family Medicine  \nDirector: [Prof. Dr. med. Birgitta Weltermann MPH](Prof. Dr. med. Birgitta Weltermann MPH)(USA)  \nTable of Contents  \nList of abbreviations ...................................................................................................... 5  \n1 Introduction.............................................................................................................. 7  \n1.1 Overview on Machine Learning .............................................................................. 7  \n1.1.1 Application of machine learning techniques in public health ............................... 8  \n1.1.2 Impact of sample-size and quality of data ........................................................... 9  \n1.1.3 Challenges of small datasets ............................................................................ 11  \n1.1.4 Machine learning model interpretation .............................................................. 13  \n1.2 Chronic stress and effects on various diseases ................................................... 14  \n1.2.1 Self-assessment questionnaires for chronic stress ........................................... 16  \n1.2.2 Chronic stress in healthcare professionals........................................................ 17  \n1.2.3 Chronic stress in the German population .......................................................... 18  \n1.2.4 Prediction of chronic stress using machine learning approaches...................... 19  \n2 Material and methods............................................................................................ 20  \n2.1 Study 1: Chronic stress in general practice assistants ......................................... 20  \n2.1.1 Dataset.............................................................................................................. 20  \n2.1.2 Primary outcome ............................................................................................... 22  \n2.1.3 Comparison of four machine learning and logistic regression models .............. 22  \n2.1.4 Model interpretation: Variable rankings in machine learning models ................ 25  \n2.1.5 Evaluation of the models' performance ............................................................. 27  \n2.2 Study 2: Chronic stress in the German population ............................................... 27  \n2.2.1 Dataset.............................................................................................................. 27  \n2.2.2 Primary outcome ............................................................................................... 29  \n2.2.3 eXtreme Gradient Boosting (XGBoost) ............................................................. 29  \n2.2.4 Model interpretation: SHapley Additive exPlanations (SHAP) ........................... 31  \n2.2.5 Evaluation of the model's performance ............................................................. 33  \n3 Results.................................................................................................................... 35  \n3.1 Study 1: Chronic stress in general practice assistants ......................................... 35  \n3.1.1 Descriptive results............................................................................................. 35  \n3.1.2 Performance of the machine learning algorithms ..........................................","cbCaicu7iYodxR8X","https://ap.wps.com/l/cbCaicu7iYodxR8X","pdf",4019588,1,103,"English","en",105,"# Introduction\n## Overview on Machine Learning\n## Chronic stress and effects on various diseases\n# Material and methods\n## Study 1: Chronic stress in general practice assistants\n## Study 2: Chronic stress in the German population\n# Results\n## Study 1: Chronic stress in general practice assistants\n## Study 2: Chronic stress in the German population\n# Discussion\n## Main findings\n## Methodological considerations on analyzing chronic stress data with ML\n## Conclusions and perspectives","[{\"question\":\"What is the main goal of the thesis?\",\"answer\":\"The thesis investigates how to derive meaningful insights for chronic stress prediction from limited data using machine learning, while addressing model interpretability and data constraints.\"},{\"question\":\"How do the two studies differ in approach and dataset?\",\"answer\":\"Study 1 focuses on chronic stress in general practice assistants and compares four machine learning models with logistic regression, while Study 2 models chronic stress in the German population using XGBoost and SHAP for interpretation.\"},{\"question\":\"Which methods are used to interpret model behavior?\",\"answer\":\"Study 1 uses variable rankings to interpret machine learning models, and Study 2 applies SHAP (SHapley Additive exPlanations) to explain feature contributions.\"}]","From Limited Data to Meaningful Insights - 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