[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124604-en":3,"doc-seo-124604-105":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},124604,1099513958762,"Logic","https://ap-avatar.wpscdn.com/avatar/1000023916a998db790?x-image-process=image/resize,m_fixed,w_180,h_180&k=1784791008015729253",8,"Research & Report","An Analysis of Frailty Progression in Elderly Patients using Process Mining and Machine Learning","Frailty is a geriatric medical condition affecting about 26% of people aged over 85 in the UK, driven by cumulative deterioration across bodily systems and reduced functional reserve. It marks a transition from healthy ageing to dependent elderly life, with limited capacity to cope with acute illness and daily stressors. This thesis analyzes frailty progression for early identification using electronic frailty index (eFI) scores. Process mining models frail elderly pathways, while machine learning determines and evaluates cut-off points related to fall, hypertension, and polypharmacy across two real healthcare datasets.","An Analysis of Frailty Progression in Elderly Patients using Process Mining and Machine  \nLearning  \nNik Fatinah Binti N. Mohd Farid  \nSubmitted in accordance with the requirements for the degree of Doctor  \nPhilosophy  \nThe University of Leeds School of Computing  \nFebruary 2023  \nAcknowledgement  \nAlhamdullilah, I praise to Allah my dear God in supporting and allowing me to experience the sweetness and bitterness of this PhD journey. I would like to express my deepest and heartfelt gratitude to my PhD supervisors, Mr Owen Johnson and Dr Marc de Kamps for their priceless and valuable advices, continuous support and encouraging comments. Their critical eyes have been the main contributing factors in pushing forward this research work. I also would like to thank my sponsor, the Ministry of Higher Education Malaysia in giving me a chance to further my study in the UK. Without their financial help, I would never have the experience of studying abroad.  \nNo words can describe how grateful and thankful I am towards the endless prayers and support I received from my beloved mother and father. My muchloved siblings, especially Nik Fatihah who always supporting me whenever the hardest and difficult time came. To Hanis Syazwani my partner through thick and thin time while we were in the UK, I am so thankful for your presence and support. Not to forget, the singLeeds ladies who always together in exploring this marvelous PhD journey.  \nI am very fortunate to be given an opportunity to work and experience this journey within the process mining healthcare research team lead by Mr Owen Johnson. Thank you to Angelina Kurniati, Guntur Kusuma, Samantha Skyes and Amirah Alharbi for sharing their experiences working in process mining field and giving boundless ideas. I also would like to thank Bradford Institute of Health Research in giving me opportunity and guidance in working with their great team and endless support in working with healthcare data.  \nAbstract  \nFrailty is a geriatric medical condition which affect 26% of people with age over 85 in the UK. This distinctive health state happened as a result of cumulative deterioration in bodily systems and diminished clinical state functional reserve over lifetime. It is a transition state from healthy ageing to dependent elderly life. The transitioning happened as the elderly with frailty has low ability to cope with acute illnesses or daily stressors. Understanding frailty progression as part of early identification of frailty may offer great opportunity in lengthening the transition time and maximize healthy ageing period. Majority of user in the healthcare sector identified as the elderly. The abundance of data recorded in the electronic healthcare record (EHR) related to patient health status and conditions has potential to facilitate the understanding of frailty. This thesis used machine learning and process mining techniques which is an emerging data-driven analytic approaches to understand frailty progression, its association with three frailty deficits of concern namely fall, hypertension and polypharmacy and investigate the variation between two frailty scores cut-off points. The main objective in this work is to analyse frail elderly pathway with respect to frailty progression pathway using electronic frailty index (eFI) score and highlight the feasibility of employing process mining and machine learning in analysing frailty. This work used two real-life healthcare datasets to understand the variability of frailty progression pathway. The first dataset is a publicly open healthcare dataset from the tertiary hospital setting in the USA. The first dataset provides as a platform for preliminary work and develop methods in analysing frailty trajectories to support reproducibility of the work. The second dataset is a UK healthcare dataset from the primary care setting. The experiments in the second dataset used as an improvement to comprehensively study frailty progression from the prev","cbCairWr53NJi7r1","https://ap.wps.com/l/cbCairWr53NJi7r1","pdf",6795195,1,245,"English","en",105,"# Acknowledgement\n# Abstract\n# Introduction\n## Overview of Research\n## The Elderly and Ageing Population\n## Frailty and its Impact\n## Exploiting EHR data using Data-Driven and Process-based Approach\n## Problem Statement\n## Research Aim, Objectives, and Questions\n## Study Approach","[{\"question\":\"What is the main purpose of analyzing frailty progression in elderly patients?\",\"answer\":\"The thesis aims to support early identification by analyzing frailty progression pathways and extending the period of healthy ageing.\"},{\"question\":\"Which methods are used to study frailty progression?\",\"answer\":\"Process mining is used to analyze frailty progression pathways, while machine learning is applied to identify and evaluate frailty score cut-off points.\"},{\"question\":\"Which datasets and frailty deficits are included in the study?\",\"answer\":\"Two real healthcare datasets are used: a publicly available tertiary-hospital dataset in the USA and a UK primary-care dataset. The analysis focuses on three deficits of concern: fall, hypertension, and polypharmacy.\"}]","An Analysis of Frailty Progression in Elderly Patients using Process Mining and Machine Learning | PDF",1785893265,617,{"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":86,"head_meta":88,"extra_data":90,"updated_unix":28},"an-analysis-of-frailty-progression-in-elderly-patients-using-process-mining-and-machine-learning","",{"@graph":36,"@context":85},[37,54,68],{"@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/an-analysis-of-frailty-progression-in-elderly-patients-using-process-mining-and-machine-learning/124604/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the main purpose of analyzing frailty progression in elderly patients?","Question",{"text":75,"@type":76},"The thesis aims to support early identification by analyzing frailty progression pathways and extending the period of healthy ageing.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which methods are used to study frailty progression?",{"text":80,"@type":76},"Process mining is used to analyze frailty progression pathways, while machine learning is applied to identify and evaluate frailty score cut-off points.",{"name":82,"@type":73,"acceptedAnswer":83},"Which datasets and frailty deficits are included in the study?",{"text":84,"@type":76},"Two real healthcare datasets are used: a publicly available tertiary-hospital dataset in the USA and a UK primary-care dataset. The analysis focuses on three deficits of concern: fall, hypertension, and polypharmacy.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]