[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122817-en":3,"doc-seo-122817-105":30,"detail-sidebar-cat-0-en-105":95},{"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},122817,3848291630094,"Emma Wilson","https://eur-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","Developing machine learning solutions for healthcare problems - dissertation requirements","Doctoral dissertation analyzing how machine learning methods can support healthcare-related prediction and classification tasks. The work presents multiple algorithmic frameworks, including an ensemble selection approach for modeling variance patterns in HIV-1 Env, a stacking-based method aimed at predicting ICU admission for hospitalized COVID-19 patients, and a hybrid machine learning–optimization approach using neural networks to distinguish super-agers from cognitive decliners. It also includes dataset preparation, evaluation metrics, hyperparameter analysis, comparative discussions, and reported results across each study.","Developing machine learning solutions for healthcare problems  \nby  \nMohammad Fili  \nA dissertation submitted to the graduate faculty  \nin partial fulfillment of the requirements for the degree of  \nDOCTOR OF PHILOSOPHY  \nMajor: Industrial Engineering  \nProgram of Study Committee:  \nGuiping Hu, Major Professor  \nKris De Brabanter  \nQing Li  \nSigurdur Olafsson  \nCameron MacKenzie  \nThe student author, whose presentation of the scholarship herein was approved by the program of study committee, is solely responsible for the content of this dissertation. The Graduate College will ensure this dissertation is globally accessible and will not permit alterations after a degree is  \nconferred.  \nIowa State University  \nAmes, Iowa  \n2022  \nCopyright © Mohammad Fili, 2022 . All rights reserved.  \nii  \nDEDICATION  \nI would like to dedicate this dissertation to my mom and dad without whose support I would not have been able to complete this work.  \niii  \nTABLE OF CONTENTS  \nPage  \n[LIST OF TABLES .......................................... vi](LIST OF TABLES .......................................... vi)  \n[LIST OF FIGURES ......................................... vii](LIST OF FIGURES ......................................... vii)  \n[ACKNOWLEDGMENTS ...................................... x](ACKNOWLEDGMENTS ...................................... x)  \n[ABSTRACT ............................................. xi](ABSTRACT ............................................. xi)  \n[CHAPTER 1. GENERAL INTRODUCTION .......................... 1](CHAPTER 1. GENERAL INTRODUCTION .......................... 1)  \n[1.1 References .......................................... 4](1.1 References .......................................... 4)  \nCHAPTER 2 . A NEW CLASSIFICATION METHOD BASED ON DYNAMIC ENSEMBLE SELECTION AND ITS APPLICATION TO PREDICT VARIANCE PATTERNS IN HIV-1 ENV ............................................ 9  \n2.1 Abstract ........................................... 9  \n2.2 Introduction ......................................... 10  \n2.2.1 Toward a Better Understanding of the Variance Patterns in HIV-1 Env Within the Infected Host .................................. 11  \n2.2.2 Multiple Classifier Selection Algorithms ..................... 12  \n2.3 Materials and Method ................................... 14  \n2.3.1 Datasets ....................................... 14  \n2.3.2 The KBC Method ................................. 15  \n2.3.3 Evaluation Metrics ................................. 23  \n2.4 Results and Discussion ................................... 25  \n2.4.1 Overview of the Approach ............................. 25  \n2.4.2 Prediction of variance patterns in HIV-1 Env .................. 26  \n2.4.3 KBC Hyperparameters Analysis ......................... 33  \n2.4.4 Effect of Base Learners in the KBC Algorithm ................. 34  \n2.5 Conclusions ......................................... 36  \n2.6 References .......................................... 39  \nCHAPTER 3 . A STACKING-BASED CLASSIFICATION METHOD TO PREDICT ICU ADMISSION IN HOSPITALIZED COVID-19 PATIENTS .................. 47  \n3.1 Abstract ........................................... 47  \n3.2 Introduction ......................................... 48  \n3.3 Materials and Method ................................... 51  \n3.3.1 Data ......................................... 51  \n3.3.2 Data Preprocessing ................................. 53  \niv  \n3.3.3 SERNA Algorithm ................................. 54  \n3.3.4 Hyperparameters .................................. 61  \n3.4 Results and Discussions .................................. 62  \n3.4.1 Region Creation .................................. 63  \n3.4.2 Model Performance ................................. 64  \n3.4.3 Comparison with other Studies .......................... 65  \n3.5 Conclusion ......................................... 66  \n3.6 References .......................................... 67  \nCHAPTER 4 . A HYBRID MACHINE LEARNING-OPTIMIZATION ALGORI","cbCaiiwAnuyva7mn","https://ap.wps.com/l/cbCaiiwAnuyva7mn","pdf",3556188,1,123,"English","en",105,"# CHAPTER 1. GENERAL INTRODUCTION\n## 1.1 References\n# CHAPTER 2 . A NEW CLASSIFICATION METHOD BASED ON DYNAMIC ENSEMBLE SELECTION AND ITS APPLICATION TO PREDICT VARIANCE PATTERNS IN HIV-1 ENV\n## 2.1 Abstract\n## 2.2 Introduction\n## 2.2.1 Toward a Better Understanding of the Variance Patterns in HIV-1 Env Within the Infected Host\n## 2.2.2 Multiple Classifier Selection Algorithms\n## 2.3 Materials and Method\n## 2.3.1 Datasets\n## 2.3.2 The KBC Method\n## 2.3.3 Evaluation Metrics\n## 2.4 Results and Discussion\n## 2.5 Conclusions\n## 2.6 References\n# CHAPTER 3 . A STACKING-BASED CLASSIFICATION METHOD TO PREDICT ICU ADMISSION IN HOSPITALIZED COVID-19 PATIENTS\n## 3.1 Abstract\n## 3.2 Introduction\n## 3.3 Materials and Method\n## 3.3.1 Data\n## 3.3.2 Data Preprocessing\n## 3.3.3 SERNA Algorithm\n## 3.3.4 Hyperparameters\n## 3.4 Results and Discussions\n## 3.5 Conclusion\n## 3.6 References\n# CHAPTER 4 . A HYBRID MACHINE LEARNING-OPTIMIZATION ALGORITHM TO DISTINGUISH SUPER-AGERS FROM COGNITIVE DECLINERS USING NEURAL NETWORKS DATA\n## 4.1 Abstract\n## 4.2 Introduction\n## 4.3 Methods\n## 4.4 Results\n## 4.5 Discussion\n## 4.6 Conclusion\n## 4.7 Appendix A. Supplementary Figures\n## 4.8 Appendix B. SUPPLEMENTARY TEXT","[{\"question\":\"What healthcare problems does the dissertation address with machine learning?\",\"answer\":\"It targets predictive and classification tasks, including modeling variance patterns in HIV-1 Env, predicting ICU admission for hospitalized COVID-19 patients, and distinguishing super-agers from cognitive decliners.\"},{\"question\":\"How does the dissertation approach HIV-1 Env variance pattern prediction?\",\"answer\":\"It presents a new classification method based on dynamic ensemble selection, including datasets, a specific KBC method, evaluation metrics, and results with discussion and conclusions.\"},{\"question\":\"What method is used to predict ICU admission in hospitalized COVID-19 patients?\",\"answer\":\"A stacking-based classification method is used, covering data, preprocessing, the SERNA algorithm, hyperparameters, region creation, performance evaluation, and comparison with other studies.\"},{\"question\":\"How are super-agers distinguished from cognitive decliners in the dissertation?\",\"answer\":\"A hybrid machine learning–optimization algorithm with neural networks is applied, using demographic and cognitive testing data, classification setup, baseline models, evaluation metrics, and detailed results and discussion.\"}]","Developing machine learning solutions for healthcare problems - 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