[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117170-en":3,"doc-seo-117170-105":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},117170,137441390410,"Hazel","https://ap-avatar.wpscdn.com/avatar/2000252f4ab5702993?_k=1776741390130283984",8,"Research & Report","Interpretable Machine Learning for Survival Analysis","Interpretable machine learning (IML) and explainable AI (XAI) have become essential as black-box models spread, especially in survival analysis where transparency, accountability, and fairness are required for clinical decision-making and medical research. Explainability helps uncover model bias and limitations and clarifies which features drive predictions or act as risk factors. The work provides a comprehensive review of IML methods for survival outcomes, mapping them to a general IML taxonomy and detailing adaptations of common techniques. A tutorial application on breast cancer recurrence (GBSG2) demonstrates practical use.","arXiv :2403 . 10250v2 [ stat .ML] 18 Nov 2024  \nORIGINAL ARTI CLE  \nInterpretable Machine Learning for Survival Analysis  \nSophie Hanna Langbein1,2 | Mateusz Krzyziński4 | Mikołaj Spytek4 | Hubert Baniecki4,5 | Przemysław  \nBiecek4,5 | Marvin N. Wright1,2,3  \n1Leibniz Institute for Prevention Research and Epidemiology – BIPS, Bremen, Germany  \n2Faculty of Mathematics and Computer Science, University of Bremen, Germany  \n3Department of Public Health, University of Copenhagen, Denmark  \n4Faculty of Mathematics and Information Science, Warsaw University of Technology, Poland  \n5Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Poland  \nCorrespondence  \nMarvin N. Wright, Leibniz Institute for Prevention Research and Epidemiology – BIPS, Bremen, Germany [Email: wright@leibniz-bips.de](Email: wright@leibniz-bips.de)  \nFunding information  \nGerman Research Foundation (DFG), Grant Numbers: 437611051, 459360854; Polish National Science Centre, Grant Number:  \n2019/34/E/ST6/00052; Polish Ministry of Education and Science, Project Number:  \nPN/01/0087/2022  \nWith the spread and rapid advancement of black box machine learning models, the field of interpretable machine learning (IML) or explainable artificial intelligence (XAI) has become increasingly important over the last decade. This is particularly relevant for survival analysis, where the adoption of IML techniques promotes transparency, accountability and fairness in sensitive areas, such as clinical decision making processes, the development of targeted therapies, interventions or in other medical or healthcare related contexts. More specifically, explainability can uncover a survival model’s potential biases and limitations and provide more mathematically sound ways to understand how and which features are influential for prediction or constitute risk factors. However, the lack of readily available IML methods may have deterred practitioners from leveraging the full potential of machine learning for predicting time-to-event data. We present a comprehensive review of the existing work on IML methods for survival analysis within the context of the general IML taxonomy. In addition, we formally detail how commonly used IML methods, such as individual conditional expectation (ICE), partial dependence plots (PDP), accumulated local effects (ALE), different feature importance measures or Friedman’s H-interaction statistics can be adapted to survival outcomes. An application of several IML methods to data on breast cancer recurrence in the German Breast Cancer Study Group (GBSG2) serves as a tutorial or guide for researchers, on how to utilize the techniques in practice to facilitate understanding of model decisions or predictions.  \nK E Y WO R D S  \nsurvival analysis, interpretable machine learning, explainable artificial intelligence, explainability, XAI, IML  \n1 | INTRODUCTION  \nSurvival analysis is a statistical subfield which analyzes and interprets time-to-event data, i.e., examining the duration until an event of interest (e.g., death, failure) occurs in a study population, while considering the effects of censoring. Censoring refers to the event of interest not being observed for some subjects before the study is terminated. For an extensive period of time, classical parametric and semi-parametric statistical methods, exemplified by the Cox proportional hazards (CoxPH) model 1 , have held a pervasive influence over the field of survival analysis. This dominance is largely attributable to their ability to handle censored data and yield reliable inferences in the form of hazard ratios. Hazard ratios are widely considered interpretable, clarifying underlying event dynamics, rendering the CoxPH model an indispensable tool in diverse scientific domains, including epidemiology, medicine, and engineering. However, in practice, the strong assumptions of the CoxPH model are often not met, with the noncollapsibility problem potentially leading to biased crude hazard ","cbCaid9ugnCm07qy","https://ap.wps.com/l/cbCaid9ugnCm07qy","pdf",2087706,1,48,"English","en",105,"# Introduction\n## Interpretable Machine Learning and Explainability for Survival Analysis\n## Overview of Survival Analysis and Time-to-Event Data\n## Motivation: Limits of Classical Cox Models and Need for IML\n## Machine Learning Survival Models and Opacity Challenges\n## Regulatory and Clinical Requirements for Transparency","[{\"question\":\"Why is interpretable machine learning important in survival analysis?\",\"answer\":\"Survival analysis often supports sensitive decisions in clinical or healthcare settings, where transparency, accountability, and fairness are required. Explainability also helps reveal potential biases, limitations, and which features influence risk predictions.\"},{\"question\":\"What limitations motivate moving beyond classical Cox proportional hazards models?\",\"answer\":\"Although CoxPH models provide interpretable hazard ratios and handle censoring, their strong assumptions are frequently violated in practice. This can lead to biased hazard ratio estimates, and interpretability challenges persist even when assumptions hold.\"},{\"question\":\"How does the document propose using IML methods for survival outcomes?\",\"answer\":\"It reviews existing IML approaches within a general IML taxonomy and formally describes how popular interpretability techniques—such as ICE, PDP, ALE, feature importance measures, and Friedman’s H-interactions—can be adapted to time-to-event targets. It also demonstrates usage through an application to breast cancer recurrence data.\"}]","Interpretable Machine Learning for Survival Analysis | PDF",1785674204,121,{"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},"interpretable-machine-learning-for-survival-analysis","",{"@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/interpretable-machine-learning-for-survival-analysis/117170/",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},"Why is interpretable machine learning important in survival analysis?","Question",{"text":76,"@type":77},"Survival analysis often supports sensitive decisions in clinical or healthcare settings, where transparency, accountability, and fairness are required. Explainability also helps reveal potential biases, limitations, and which features influence risk predictions.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What limitations motivate moving beyond classical Cox proportional hazards models?",{"text":81,"@type":77},"Although CoxPH models provide interpretable hazard ratios and handle censoring, their strong assumptions are frequently violated in practice. This can lead to biased hazard ratio estimates, and interpretability challenges persist even when assumptions hold.",{"name":83,"@type":74,"acceptedAnswer":84},"How does the document propose using IML methods for survival outcomes?",{"text":85,"@type":77},"It reviews existing IML approaches within a general IML taxonomy and formally describes how popular interpretability techniques—such as ICE, PDP, ALE, feature importance measures, and Friedman’s H-interactions—can be adapted to time-to-event targets. 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