[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120600-en":3,"doc-seo-120600-105":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":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},120600,13056703020460,"Valentina","https://ap-avatar.wpscdn.com/avatar/be000253dac470eee5d?_k=1778207105932848923",8,"Research & Report","New horizons in prediction modelling using machine learning in older people’s healthcare research","Machine learning (ML) and prediction modelling increasingly shape healthcare by offering actionable insights and supporting clinical decisions, especially in the era of big data. This paper acts as an introductory guide for health researchers, explaining how ML-based prediction models are developed, assessed, and reported across the full research workflow. It covers key prediction types and ML approaches (supervised, unsupervised, semi-supervised), highlights theoretical concepts, and emphasizes data quality, preprocessing, and unbiased evaluation.","King’s Research Portal  \nDOI:  \n10.1093/ageing/afae201  \nDocument Version  \nPublisher's PDF, also known as Version of record  \nLink to publication record in King's Research Portal  \nCitation for published version (APA):  \nStahl, D. (2024) . New horizons in prediction modelling using machine learning in older people's healthcare research. Age and Ageing, 53(9), Article afae201 . [https://doi.org/10.1093/ageing/afae201](https://doi.org/10.1093/ageing/afae201)  \nCiting this paper  \nPlease note that where the full-text provided on King's Research Portal is the Author Accepted Manuscript or Post-Print version this may differ from the final Published version. If citing, it is advised that you check and use the publisher's definitive version for pagination, volume/issue, and date of publication details. 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Mar. 2026  \nAge and Ageing 2024; 53: afae201  \n[https://doi.org/10.1093/ageing/afae201](https://doi.org/10.1093/ageing/afae201)  \n© The Author(s) 2024 . Published by Oxford University Press on behalf of the British Geriatrics Society. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.  \nNEW HORIZONS  \nNew horizons in prediction modelling using machine learning in older people’s healthcare research  \nDaniel Stahl  \nDepartment of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK  \nAddress correspondence to: Daniel Stahl, Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology  \n& Neuroscience, King’s College London, London, [UK. Email: daniel.r.stahl@kcl.ac.uk](UK. Email: daniel.r.stahl@kcl.ac.uk)  \nAbstract  \nMachine learning (ML) and prediction modelling have become increasingly influential in healthcare, providing critical insightsand supporting clinical decisions, particularly in the age of big data. This paper serves as an introductory guide for health researchers and readers interested in prediction modelling and explores how these technologies support clinical decisions, particularly with big data, and covers all aspects of the development, assessment and reporting of a model using ML. The paper starts with the importance of prediction modelling for precision medicine. It outlines different types of prediction and machine learning approaches, including supervised, unsupervised and semi-supervised learning, and provides an overview of popular algorithms for various outcomes and settings. It also introduces key theoretical ML concepts. The importance of data quality, preprocessing and unbiased model performance evaluation is highlighted. Concepts of apparent, internal and external validation will be introduced along with metrics fo","cbCaio4FlcQ3XFTf","https://ap.wps.com/l/cbCaio4FlcQ3XFTf","pdf",601158,1,13,"English","en",105,"# Key points\n## Precision medicine and model choice\n## Development workflow and validation\n## Stakeholder involvement\n# Introduction\n## Evidence-based medicine vs precision medicine\n## Heterogeneity and individual treatment response\n# Abstract\n## Purpose and scope of the guide\n## ML approaches and model evaluation","[{\"question\":\"What is the main purpose of this paper on prediction modelling?\",\"answer\":\"It provides an introductory guide for health researchers, explaining how ML and prediction modelling support clinical decisions and how models should be developed, assessed, and reported.\"},{\"question\":\"Which machine learning approaches are discussed in the paper?\",\"answer\":\"The paper outlines supervised, unsupervised, and semi-supervised learning approaches and reviews popular algorithms for different outcomes and settings.\"},{\"question\":\"Why is internal validation important in clinical prediction models?\",\"answer\":\"Internal validation without data leakage is crucial for producing unbiased estimates of a model’s performance.\"}]","New horizons in prediction modelling using machine learning in older people’s healthcare research | 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