[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128785-en":3,"doc-seo-128785-105":31,"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":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},128785,1099523882367,"Hazel","https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc",8,"Research & Report","Learning effective machine learning models for clinical applications in psychiatry - Doctor of Philosophy thesis","A doctoral thesis examines the diagnostic landscape in psychiatry and the potential of precision psychiatry to improve illness classification. The work reviews AI and psychiatry literature, then presents three ML-focused academic studies across five chapters. It applies machine learning to detect first-episode Bipolar Disorder using cognitive tests, builds a learned model to identify childhood anxiety via brain dysfunction during emotional facial expression processing, and uses NLP sentiment analysis to detect PTSD from semi-structured interview text, comparing results with existing research.","Learning effective machine learning models for clinical applications in psychiatry  \nby  \nJeffrey Sawalha  \nA thesis submitted in partial fulfillment of the requirements for the degree of  \nDoctor of Philosophy  \nDepartment of Psychiatry  \nUniversity of Alberta  \n© Jeffrey Sawalha, 2023  \nAbstract  \nThis is a comprehensive examination of the diagnostic landscape in psychiatry and the role precision psychiatry might play in redefining how we classify illnesses. This thesis delves into three areas that currently exist in psychiatry today and is divided into five chapters. The first and second chapters review the literature and clinical space of AI and psychiatry. The last three chapters consist of academic articles that explore the use of AI in the three problematic areas of psychiatry.  \nThe third chapter involves the early stage detection of Bipolar Disorder (Type 1) using cognitive assessments. Identifying cognitive dysfunction in the early stages of Bipolar Disorder (BD) can allow for early intervention. Previous studies have shown a strong correlation between cognitive dysfunction and the number of manic episodes. The objective of this study was to apply machine learning (ML) techniques on a battery of cognitive tests to identify first-episode BD patients (FE-BD) . Specifically, we wanted to know if we could make generalized predictions about the various stages of BD using cognitive tests.  \nThe fourth chapter examines childhood anxiety and produces a learned model that can detect dysfunction in the brains of children while they examine emotional facial expressions. Childhood anxiety is a difficult disorder to diagnose due to validity controversies and the conflation of normal developmentalbehavioral patterns with anxiety symptoms. Our study not only seeks to train a model that can distinguish anxious from non-anxious children, but also to  \ndiscover neural markers related to this diagnosis.  \nLastly, the fifth chapter utilizes natural language processing to detect the presence of post-traumatic stress disorder (PTSD) . Specifically, we utilize sentiment analysis, a sub area of natural language processing (NLP), to extract emotional content from text information. In our study, we train an ML model on text data, which is part of the Audio/Visual Emotion Challenge and Workshop (AVEC-19) corpus, to identify individuals with PTSD using sentiment analysis from semi-structured interviews. We sought to understand the emotional spectrum of language and compare our findings with the ongoing literature.  \nTogether, each of these studies illustrate how ML can be used to augment clinical decision-making surrounding the underlying conditions of individuals who may suffer from these illnesses. In doing so, we provide a conceptual review of the current barriers that exist in precision psychiatry today. Our hope is to provide the reader with a foundation of how ML can be used in psychiatry, while also highlighting some of the current barriers that hold back this field today. This comes in the form of a conceptual review (Chapter 2) and sets the landscape for the three published articles included.  \nPreface  \nSome research conducted for this thesis forms part of an international research collaboration. All 3 papers are collaborations with different institutions across the globe. All secondary analyses received approval from the University of Alberta’s Health Research Ethics Board (Pro 00072946) .  \nFirst, the group at the University of Alberta includes Dr. Andrew Greenshaw, Dr. Russell Greiner, Dr. Bo Cao, Dr. Mohammed Yousefnezhad, Dr. Matthew Brown, Dr. Alessandro Selvitella (Purdue University) and Zehra Shah. All members played a pivotal role in one of the three papers. Dr. Greenshaw and Dr. Greiner were the main principal investigators for all content in this thesis.  \nChapter 3 was a collaborative project with Dr. Tao Li from the Affiliated Brain Hospital of Guangzhou Medical University at Guangzhou Huiai Hospital in China. The data we","cbCaigxhbRuvh6hb","https://ap.wps.com/l/cbCaigxhbRuvh6hb","pdf",5591552,4,1,145,"English","en",105,"# Abstract\n# Preface\n# Acknowledgements\n# Chapter 1-2: Literature review on AI and psychiatry\n# Chapter 3: Early detection of Bipolar Disorder using cognitive assessments\n# Chapter 4: Childhood anxiety detection via brain dysfunction and emotional facial expressions\n# Chapter 5: PTSD detection using natural language processing sentiment analysis","[{\"question\":\"What is the central goal of this thesis in psychiatry?\",\"answer\":\"To examine psychiatry’s diagnostic landscape and evaluate how precision psychiatry and machine learning can refine illness classification.\"},{\"question\":\"How does the thesis detect early Bipolar Disorder?\",\"answer\":\"It applies machine learning to a battery of cognitive tests to identify first-episode Bipolar Disorder and make generalized predictions across stages.\"},{\"question\":\"Which methods are used to detect PTSD and childhood anxiety?\",\"answer\":\"PTSD is detected using NLP sentiment analysis on semi-structured interview text. Childhood anxiety is addressed by training a learned model to detect dysfunction in children’s brains while they process emotional facial expressions.\"}]","Learning effective machine learning models for clinical applications in psychiatry - Doctor of Philosophy thesis | PDF",1786003431,365,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"learning-effective-machine-learning-models-for-clinical-applications-in-psychiatry-doctor-of-philosophy-thesis","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/research-report/",3,{"item":53,"name":13,"@type":44,"position":20},"https://docshare.wps.com/document/learning-effective-machine-learning-models-for-clinical-applications-in-psychiatry-doctor-of-philosophy-thesis/128785/",{"url":53,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-25","2026-08-06",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},"What is the central goal of this thesis in psychiatry?","Question",{"text":76,"@type":77},"To examine psychiatry’s diagnostic landscape and evaluate how precision psychiatry and machine learning can refine illness classification.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How does the thesis detect early Bipolar Disorder?",{"text":81,"@type":77},"It applies machine learning to a battery of cognitive tests to identify first-episode Bipolar Disorder and make generalized predictions across stages.",{"name":83,"@type":74,"acceptedAnswer":84},"Which methods are used to detect PTSD and childhood anxiety?",{"text":85,"@type":77},"PTSD is detected using NLP sentiment analysis on semi-structured interview text. Childhood anxiety is addressed by training a learned model to detect dysfunction in children’s brains while they process emotional facial expressions.","https://schema.org",{"og:url":53,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":47,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]