[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119956-en":3,"doc-seo-119956-105":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},119956,4398048949847,"Eliana","https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267",8,"Research & Report","Privacy Preserving Machine Learning for Next Day Pain Prediction - A thesis","Healthcare data is constrained by strict privacy regulations, ethical requirements, and the inherent sensitivity of medical information, limiting research progress and delaying improvements in patient outcomes. This thesis investigates next-day pain prediction using machine learning under limited patient data, and evaluates synthetic data as a privacy-preserving alternative. Synthetic datasets generate realistic, non-personal records that match the statistical properties of real healthcare data. Methods are tested with and without privacy guarantees for juvenile idiopathic arthritis and lupus, training on synthetic or real data and assessing performance on real data.","©Copyright 2024 Ashutosh Vilas Engavle  \nPrivacy Preserving Machine Learning for Next Day Pain Prediction  \nAshutosh Vilas Engavle  \nA thesis  \nsubmitted in partial fulfillment of the requirements for the degree of  \nMaster of Science  \nUniversity of Washington  \n2024  \nCommittee:  \nMartine De Cock  \nAnderson Nascimento  \nWeichao Yuwen  \nProgram Authorized to Offer Degree: Computer Science and Systems  \nUniversity of Washington  \nAbstract  \nPrivacy Preserving Machine Learning for  \nNext Day Pain Prediction  \nAshutosh Vilas Engavle  \nChair of the Supervisory Committee:  \nMartine De Cock  \nSchool of Engineering and Technology  \nThe availability of healthcare data is critically limited due to stringent privacy regulations, ethical considerations, and the intrinsic sensitivity of medical information. This scarcity hampers research and development in medical science, ultimately affecting the advancement of healthcare services and patient outcomes. Synthetic data emerges as a potent solution to this challenge, offering a pathway to bolster data accessibility while safeguarding patient privacy. This thesis explores the multifaceted issue of next day pain prediction with machine learning models and the limited availability of patient data to train such models, and delves into the potential of synthetic data to bridge this gap. By generating realistic, non-personal data that mimics the statistical properties of real healthcare datasets, synthetic data provides a viable alternative for research and analysis, circumventing privacy concerns. We use methodologies for synthetic data generation with and without privacy, and evaluate their effectiveness and utility for next day pain prediction in patients with Juvenile Idiopathic Arthritis and lupus. We compare the utility of synthetic data with that of real data by training models on both kinds of data and evaluating the trained models on real data. The findings indicate that machine learning models are able to do next day pain prediction. We also see that marginal based synthetic data generation methods can create synthetic data with good utility with substantial privacy guarantee for this task.  \nTABLE OF CONTENTS  \nPage  \nChapter 1: Introduction ................................ 1  \n1.1 Background .................................... 1  \n1.2 Research Focus .................................. 2  \n1.3 Novelty and Impact ................................ 3  \nChapter 2: Related Work ................................ 5  \n2.1 Machine Learning for Symptom Prediction ................... 5  \n2.2 Machine Learning with Actigraphy Data .................... 6  \n2.3 Synthetic Data Generation ............................ 6  \nChapter 3: Data and Problem Description ...................... 8  \n3.1 Introduction .................................... 8  \n3.2 Raw Data Description .............................. 8  \n3.3 Problem Description: Machine Learning Task Definition ........... 17  \nChapter 4: Methodology ................................ 24  \n4.1 Data Filtering and Preprocessing ........................ 24  \n4.2 Classifier and Parameters Overview ....................... 32  \n4.3 Synthetic Data Generation ............................ 35  \nChapter 5: Results ................................... 40  \n5.1 SIPA Dataset ................................... 40  \n5.2 Todd Dataset ................................... 46  \n5.3 SLE Dataset .................................... 48  \n5.4 Synthetic Data Generation Results for SLE ................... 51  \n5.5 Combined Dataset Results ............................ 69  \nChapter 6: Conclusion & Future Work ........................ 73  \nBibliography ........................................ 75  \nACKNOWLEDGMENTS  \nI would like to extend my deepest gratitude to a number of individuals whose support and guidance have been invaluable throughout the course of this endeavor.  \nFirst and foremost, I must express my profound appreciation to my parents. Their unwavering belief in my abilities,","cbCaikMcslwH2yz7","https://ap.wps.com/l/cbCaikMcslwH2yz7","pdf",8895383,1,83,"English","en",105,"# Chapter 1: Introduction\n## Background\n## Research Focus\n## Novelty and Impact\n# Chapter 2: Related Work\n## Machine Learning for Symptom Prediction\n## Machine Learning with Actigraphy Data\n## Synthetic Data Generation\n# Chapter 3: Data and Problem Description\n## Raw Data Description\n## Problem Description: Machine Learning Task Definition\n# Chapter 4: Methodology\n## Data Filtering and Preprocessing\n## Classifier and Parameters Overview\n## Synthetic Data Generation\n# Chapter 5: Results\n## SIPA Dataset\n## Todd Dataset\n## SLE Dataset\n## Synthetic Data Generation Results for SLE\n## Combined Dataset Results\n# Chapter 6: Conclusion & Future Work","[{\"question\":\"Why is synthetic data considered for next day pain prediction?\",\"answer\":\"Healthcare data availability is limited by privacy regulations and the sensitivity of medical records. Synthetic data aims to improve accessibility while avoiding disclosure of personal patient information.\"},{\"question\":\"How is synthetic data evaluated in the thesis?\",\"answer\":\"Models are trained using synthetic data and real data, then evaluated on real data to compare utility and predictive performance for next day pain prediction.\"},{\"question\":\"What patient populations are studied?\",\"answer\":\"The thesis evaluates next day pain prediction for patients with juvenile idiopathic arthritis and lupus, including dataset-specific results.\"}]","Privacy Preserving Machine Learning for Next Day Pain Prediction - A thesis | PDF",1785727190,209,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"privacy-preserving-machine-learning-for-next-day-pain-prediction-a-thesis","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/privacy-preserving-machine-learning-for-next-day-pain-prediction-a-thesis/119956/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04","2026-08-03",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Why is synthetic data considered for next day pain prediction?","Question",{"text":76,"@type":77},"Healthcare data availability is limited by privacy regulations and the sensitivity of medical records. Synthetic data aims to improve accessibility while avoiding disclosure of personal patient information.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How is synthetic data evaluated in the thesis?",{"text":81,"@type":77},"Models are trained using synthetic data and real data, then evaluated on real data to compare utility and predictive performance for next day pain prediction.",{"name":83,"@type":74,"acceptedAnswer":84},"What patient populations are studied?",{"text":85,"@type":77},"The thesis evaluates next day pain prediction for patients with juvenile idiopathic arthritis and lupus, including dataset-specific results.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":46,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":46,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]