[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122652-en":3,"doc-seo-122652-105":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},122652,687197100911,"Himbo","https://ap-avatar.wpscdn.com/avatar/a000239b6f1da00475?x-image-process=image/resize,m_fixed,w_180,h_180&k=1785132997149421697",8,"Research & Report","Predicting Alzheimer’s Disease Diagnosis Risk over Time with Survival Machine Learning on the ADNI Cohort - Abstract","The paper addresses the urgent need for efficient tools to predict Alzheimer’s Disease (AD) deterioration and the likely time until deterioration leading to dementia. It investigates survival machine learning models built on the ADNI cohort, extending beyond classification to estimate time-dependent risk while accounting for censored observations. Results show strong predictive performance with a reported 0.86 C-Index, supporting clinical investigation and risk prediction use.","Predicting Alzheimer’s Disease Diagnosis Risk over Time with Survival Machine Learning on the ADNI Cohort  \nHenry Musto1 , Daniel Stamate1, 2 , Ida Pu 1, Daniel Stahl 3  \n1 Data Science & Soft Computing Lab, Computing Department, Goldsmiths College, University of London, United Kingdom  \n2 Division of Population Health, Health Services Research & Primary Care, School of Health Sciences, University of Manchester, United Kingdom  \n3 Institute of Psychiatry Psychology and Neuroscience, Biostatistics and Health Info  \nmatics Department, King’s College London, London, UK  \nAbstract. The rise of Alzheimer’s Disease worldwide has prompted a search for efficient tools which can be used to predict deterioration in cognitive decline leading to dementia. In this paper, we explore the potential of survival machine learning as such a tool for building models capable of predicting not only deterioration but also the likely time to deterioration. We demonstrate good predictive ability (0.86 C-Index), lending support to its use in clinical investigation and prediction of Alzheimer’s Disease risk.  \nKeywords: Survival Machine Learning, ADNI, Clinical Prediction Modelling.  \n1 Introduction  \nOne of the most pressing challenges for governments and healthcare systems is the rising number of people with dementia. More than 55 million people live with dementia worldwide, and there are nearly 10 million new cases yearly, with 60-70% of all dementias being of Alzheimer’s Disease type (AD) [1] . Recently, attention has turned to Machine Learning (ML) as a tool for improving the predictive ability of clinical models concerning AD and addressing clinical challenges more widely. However, of the hundreds of clinical ML models that appear in scientific publications each year, few have thus far been successfully embedded into existing clinical practice [2] . Oneof the reasons for this is that most models only provide predictions for disease cases without quantifying the probability of disease occurrence. This limitation restricts clinicians' ability to accurately measure and communicate the probability of disease development over time with the patient. [3]. Also, in the context of predicting the progression of AD in particular, many studies that use ML methods employ a classification approach, whereby the outcome to be predicted is either a binomial or multinomial outcome within a specific timeframe [4][5] . The datasets are often derived from longitudinal studies, whereby clinical marker data is collected from participants over months and years [6] . Thus, such data has a temporal element inherent to the methodology employed in the collection process. However, standard classification ML cannot consider the predictive power of time in conjunction with other predictors. Furthermore, classification models cannot handle drop-outs which are common in longitudinal studies.  \n2  \nWith this in mind, a newly emerging field of exploration seeks to build on traditional time-dependent statistical models, such as survival analysis, to develop machine learning models which can predict the time-dependent risk of developing AD and go beyond simple classification. Survival analysis is a statistical method that aims to predict the risk of an event's occurrence, such as death or the emergence of a disease, as a function of time. A key aspect of survival analysis is the presence of censored data, indicating that the event of interest has not occurred while the subject was part of the study. The presence of censored data requires the use of specialised techniques. Traditionally, the Cox proportional hazards model [7] has been the most widely used technique for analysing data containing also censored records. However, the Cox model typically works well for small data sets and does not scale well to high dimensions [8] . ML techniques that inherently handle high-dimensional data have been adapted to handle censored data, allowing ML to offer a more flexible alternative for analy","cbCaijPYVwUZGq4Z","https://ap.wps.com/l/cbCaijPYVwUZGq4Z","pdf",120113,1,13,"English","en",105,"# Introduction\n## Clinical motivation and limitations of existing ML\n## Why survival analysis for time-dependent risk\n# Related Work\n## Survival-based ML comparisons for AD risk over time","[{\"question\":\"Why do traditional clinical ML classification models struggle with AD progression prediction?\",\"answer\":\"They often predict only a fixed-time binomial or multinomial outcome, without quantifying the probability over time, and they cannot naturally handle drop-outs common in longitudinal data.\"},{\"question\":\"What makes survival machine learning suitable for this task?\",\"answer\":\"It models time-dependent risk and incorporates censored data, where the event has not occurred during a subject’s follow-up, enabling prediction of when deterioration may happen.\"},{\"question\":\"Which dataset and evaluation result are reported for the proposed approach?\",\"answer\":\"The study uses the ADNI cohort and reports a predictive ability of 0.86 C-Index, indicating effective risk prediction for AD deterioration over time.\"}]","Predicting Alzheimer’s Disease Diagnosis Risk over Time with Survival Machine Learning on the ADNI Cohort - Abstract | PDF",1785811952,33,{"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},"predicting-alzheimers-disease-diagnosis-risk-over-time-with-survival-machine-learning-on-the-adni-cohort-abstract","",{"@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/predicting-alzheimers-disease-diagnosis-risk-over-time-with-survival-machine-learning-on-the-adni-cohort-abstract/122652/",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","2026-08-04",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},"Why do traditional clinical ML classification models struggle with AD progression prediction?","Question",{"text":75,"@type":76},"They often predict only a fixed-time binomial or multinomial outcome, without quantifying the probability over time, and they cannot naturally handle drop-outs common in longitudinal data.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What makes survival machine learning suitable for this task?",{"text":80,"@type":76},"It models time-dependent risk and incorporates censored data, where the event has not occurred during a subject’s follow-up, enabling prediction of when deterioration may happen.",{"name":82,"@type":73,"acceptedAnswer":83},"Which dataset and evaluation result are reported for the proposed approach?",{"text":84,"@type":76},"The study uses the ADNI cohort and reports a predictive ability of 0.86 C-Index, indicating effective risk prediction for AD deterioration over time.","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"]