[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119338-en":3,"doc-seo-119338-105":30,"detail-sidebar-cat-0-en-105":92},{"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":20,"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},119338,1374391975076,"Riley","https://ap-avatar.wpscdn.com/avatar/14000253ca4ec9f6853?x-image-process=image/resize,m_fixed,w_180,h_180&k=1783305029341752051",8,"Research & Report","Derivation and Validation of a Machine Learning Model for the Prevention of Unplanned Dialysis","Unplanned kidney dialysis correlates with elevated morbidity and mortality in advanced chronic kidney disease patients, and its occurrence is shaped by many modifiable factors through timely intervention and treatment planning. This thesis investigates clinical machine learning models that predict kidney failure within short horizons of 6 and 12 months to define an optimal dialysis preparation timeline. It outlines dataset characterization, data processing, and a comparison between machine learning and traditional approaches, with results indicating potential for meaningful reduction of unplanned dialysis burden in advanced CKD centers.","Derivation and Validation of a Machine Learning Model for the Prevention of Unplanned Dialysis  \nby  \nMartin M. Klamrowski, B.Eng.  \nA thesis submitted to the Faculty of Graduate and Postdoctoral Affairs in partial fulfillment of the requirements for the degree of  \nMaster of Applied Science  \nin  \nElectrical and Computer Engineering  \n(Data Science Specialization)  \nCarleton University  \nOttawa, Ontario  \n© 2023, Martin M. Klamrowski  \nAbstract  \nUnplanned kidney dialysis is associated with higher morbidity and mortality rates among advanced chronic kidney disease patients. The incidence of unplanned dialysis can be attributed to myriad factors, but importantly, many of them are modifiable with appropriately timed intervention and treatment planning. A system tailored to the clinical question of the optimal dialysis preparation timeline could be crucial in mitigating the risk factors associated with initiating dialysis in an unplanned manner. Hereinafter, the development of clinical machine learning models for the prediction of kidney failure over short timeframes of 6 and 12 months is studied. The groundwork for the machine learning analysis is laid out, covering the characterization of The Ottawa Hospital’s Multi Care Kidney Clinic dataset, the data processing, and a comparison of machine learning to traditional methods. We find that a data-driven approach proffers an opportunity to significantly reduce the burden of unplanned dialysis in advanced CKD centers.  \nAcknowledgements  \nMy sincerest thanks go to my graduate advisors, Drs. Ran Klein, Jim Green, Greg Hundemer, and Ayub Akbari. I am indebted to them, as to the additional exceptional research collaborators who are building this project from the ground up – Drs. Chris McCudden and Babak Rashidi. Thank you for all that you do.  \nI am also thankful to Suzanne Jackson, Melanie Bujold, and Drs. Amber Molnar, Tim Ramsay, Fateme Rajabi, Cedric Edwards, Alissa Visram, and Matthew Oliver for the essential assistance and expertise provided along the way.  \nThank you to the Canadian Institutes for Health Research for funding this project.  \nThank you to my thesis committee, Drs. Ted Perkins, Jeff Gilchrist, and Sreeraman Rajan for taking the time to critically review and evaluate this thesis.  \nTable of Contents  \nAbstract............................................................................................................................. i  \nAcknowledgements ........................................................................................................ ii  \nTable of Contents ........................................................................................................... iii  \nList of Tables .................................................................................................................. iv  \nTable of Figures ..............................................................................................................v  \nList of Abbreviations ...................................................................................................viii  \nChapter 1: Introduction ................................................................................................. 1  \n1.1 Motivation .................................................................................................. 1  \n1.2 Problem Statement .................................................................................... 1  \n1.3 Contributions .............................................................................................2  \n1.4 Thesis Structure.........................................................................................3  \nChapter 2: Background ................................................................................................5  \n2.1 Chronic Kidney Disease ............................................................................5  \n2.2 Survival Analysis........................................................................","cbCaikS3mJDOAQ4U","https://ap.wps.com/l/cbCaikS3mJDOAQ4U","pdf",12069558,1,184,"English","en",105,"# Abstract\n# Acknowledgements\n# Table of Contents\n# List of Tables\n# List of Figures\n# List of Abbreviations\n# Chapter 1: Introduction\n## Motivation\n## Problem Statement\n## Contributions\n## Thesis Structure\n# Chapter 2: Background\n## Chronic Kidney Disease\n## Survival Analysis\n## Machine Learning\n## Conclusion\n# Chapter 3: Dataset\n## Overview\n## The Ottawa Hospital Multi-Care Kidney Clinic Dataset\n## Characteristics\n## Missing Data\n## Feature Engineering\n## Modeling\n## Conclusion\n# Chapter 4: Comparison of Cox Regression and Machine Learning for Short Timeframe Prediction of Kidney Failure among Advanced CKD Patients\n## Preamble\n## Methods\n## Results\n## Discussion\n# Chapter 5: Derivation and Validation of a Machine Learning Model for the Prevention of Unplanned Dialysis among Patients with Advanced CKD\n## Preamble","[{\"question\":\"Why is preventing unplanned dialysis important for advanced CKD patients?\",\"answer\":\"Unplanned kidney dialysis is associated with higher morbidity and mortality rates in advanced chronic kidney disease patients, so timely preparation can mitigate risk.\"},{\"question\":\"What prediction horizons are studied in the thesis?\",\"answer\":\"The machine learning models are developed to predict kidney failure over short timeframes of 6 and 12 months.\"},{\"question\":\"What does the thesis compare to evaluate the machine learning approach?\",\"answer\":\"It compares machine learning methods against traditional methods, including Cox regression, using the Ottawa Hospital Multi Care Kidney Clinic dataset.\"}]","Derivation and Validation of a Machine Learning Model for the Prevention of Unplanned Dialysis | 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is preventing unplanned dialysis important for advanced CKD patients?","Question",{"text":76,"@type":77},"Unplanned kidney dialysis is associated with higher morbidity and mortality rates in advanced chronic kidney disease patients, so timely preparation can mitigate risk.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What prediction horizons are studied in the thesis?",{"text":81,"@type":77},"The machine learning models are developed to predict kidney failure over short timeframes of 6 and 12 months.",{"name":83,"@type":74,"acceptedAnswer":84},"What does the thesis compare to evaluate the machine learning approach?",{"text":85,"@type":77},"It compares machine learning methods against traditional methods, including Cox regression, using the Ottawa Hospital Multi Care Kidney Clinic 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