[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117508-en":3,"doc-seo-117508-105":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},117508,1099514067438,"River Wang","https://ap-avatar.wpscdn.com/avatar/100002539ee87300030?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780474512215547542",8,"Research & Report","MACHINE LEARNING FOR ALS SURVIVAL ANALYSIS - Tesi di laurea magistrale","Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease with no definitive cure, where death occurs due to loss of neuron functionality. The thesis leverages the Answer ALS Data Portal, which aggregates large-scale data on ALS, related neurodegenerative conditions, asymptomatic ALS, and control patients. The work analyzes ALS survival to identify factors associated with longer or shorter lifespan and builds predictive models to estimate survival for new patients. Methods include Kaplan-Meier, Cox proportional hazards, and Random Survival Forest using C-index and Brier Score, and models supporting time-dependent covariates via Cox and DynForest.","DIPARTIMENTO DI INGEGNERIA DELL’INFORMAZIONE  \nCORSO DI LAUREA MAGISTRALE IN  \nBIOINGEGNERIA  \n“MACHINE LEARNING FOR ALS SURVIVAL ANALYSIS”  \nRelatore: Prof. Morten Gram Pedersen  \nLaureanda: Noemi Mattiello  \nAnno accademico: 2023-2024  \nData di laurea: 27-11-2024  \nContents  \n1 CHAPTER ONE 5  \n1.1 Introduction ........................................ 5  \n1.2 Epidemiology ....................................... 6  \n1.3 Medicine .......................................... 6  \n2 CHAPTER TWO 9  \n2.1 Censored Patients .................................... 9  \n2.2 Survival Analysis ..................................... 9  \n2.2.1 Kaplan-Meier Method .............................. 9  \n2.2.2 Cox Proportional-Hazards Model ........................ 10  \n2.2.3 Random Survival Forest ............................. 11  \n2.3 Statistical methods for evaluating the performance of prediction models ...... 12  \n2.3.1 Brier Score .................................... 12  \n2.3.2 Concordance Index ................................ 13  \n3 CHAPTER THREE 15  \n3.1 Database ......................................... 15  \n4 CHAPTER FOUR 18  \n4.1 Data Loading ....................................... 18  \n4.2 Creation of the dataset .................................. 19  \n4.3 Data Reorganization ................................... 25  \n4.4 Computation of the Kaplan-Meier Model ....................... 28  \n4.5 Computation of the Cox Proportional-Hazards Model ................ 35  \n4.6 Computation of the Random Forest for Survival Model ................ 46  \n5 CHAPTER FIVE 57  \n5.1 Time Dependent Covariates ............................... 57  \n5.2 The function tmerge ................................... 58  \n5.3 Creation of the dataset .................................. 58  \n5.4 Time Dependent Covariates in the Cox Model ..................... 64  \n6 CHAPTER SIX 74  \n6.1 DynForest Model ..................................... 74  \n7 CONCLUSIONS 78  \n2  \nAbstract  \nAmyotrophic lateral sclerosis (ALS) is a devastating neurodegenerative disease that slowly and progressively leads to death due to the loss of neuron functionality. Unfortunately, thereis still no de􀀌nitive cure for ALS, and the causes of the disease remain largely unknown. However, some treatments have been developed, most of them still in experimental stages, with the aim of alleviating symptoms so that patients can live as normal life as possible and, most importantly, delay death.  \nTo support this goal, the ’The Answer ALS Data Portal’ has gathered hundreds of trillions of data points concerning thousands of patients, including those a􀀋ected by ALS, patients with non-ALS neurodegenerative disease, asymptomatic ALS patients, and control patients. These data have been collected to provide researchers with resources to advance the study of the disease’s origin, as well as treatments for symptoms and potentially the disease itself.  \nThe objective of this thesis was to analyze the survival of patients with ALS and identify factors that contribute to a longer or shorter lifespan. Additionally, it aimed to develop a predictive model to 􀀌nd connections within the patient data, in order to predict survival for new patients based on their lifestyle and chosen therapies.  \nFor this purpose, survival was 􀀌rst examined using three methods that utilized time-􀀌xed information available at the baseline: Kaplan-Meier (KM), Cox Proportional Hazard, and Random Forest for Survival, Regression and Classi􀀌cation (rf-src), and their performances were assessed using two indices: the C-index and the Brier Score.  \nA recent development is the emergence of prognostic models capable of handling timedependent covariates, which have drawn signi􀀌cant interest from researchers in recent years. These models enable survival prediction over time while accounting for changes in the data during disease preogression. To analyze time-dependent covariates, Cox Proportional Hazards models and DynForest models were implemented and analyzed.  ","cbCaioFjl7oooq3D","https://ap.wps.com/l/cbCaioFjl7oooq3D","pdf",1064852,1,84,"English","en",105,"# Contents\n## Chapter One\n## Chapter Two\n## Chapter Three\n## Chapter Four\n## Chapter Five\n## Chapter Six\n## Conclusions","[{\"question\":\"What survival models are used for ALS time-fixed baseline information?\",\"answer\":\"The thesis applies Kaplan-Meier and Cox proportional-hazards models, and Random Survival Forest for survival regression/classification.\"},{\"question\":\"How is model performance evaluated?\",\"answer\":\"Performance is assessed using two indices: the C-index and the Brier Score.\"},{\"question\":\"How does the thesis handle time-dependent covariates?\",\"answer\":\"It implements models that can account for covariate changes over disease progression, including Cox models with time-dependent covariates and DynForest.\"}]","MACHINE LEARNING FOR ALS SURVIVAL ANALYSIS - Tesi di laurea magistrale | PDF",1785676463,212,{"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},"machine-learning-for-als-survival-analysis-masters-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/machine-learning-for-als-survival-analysis-masters-thesis/117508/",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","2026-08-05","2026-08-02",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 survival models are used for ALS time-fixed baseline information?","Question",{"text":76,"@type":77},"The thesis applies Kaplan-Meier and Cox proportional-hazards models, and Random Survival Forest for survival regression/classification.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How is model performance evaluated?",{"text":81,"@type":77},"Performance is assessed using two indices: the C-index and the Brier Score.",{"name":83,"@type":74,"acceptedAnswer":84},"How does the thesis handle time-dependent covariates?",{"text":85,"@type":77},"It implements models that can account for covariate changes over disease progression, including Cox models with time-dependent covariates and DynForest.","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"]