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The work addresses limitations of existing disability proxies and data-linking challenges that restrict disability-disaggregated analyses of inequities. It proposes ML-based disability markers, using unsupervised learning to classify disability groups and natural language processing to extract relevant information from clinical notes. 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This version will undergo additional copyediting, typesetting and review before it is published in its final form. As such, this version is no longer the Accepted Manuscript, but it is not yet the definitive Version of Record; we are providing this early version to give early visibility of the article. Please note that Elsevier’s sharing policy for the Published Journal Article applies to this version, see: [https://www.elsevier.com/about/policies-and](https://www.elsevier.com/about/policies-and)standards/sharing\\#4-published-journal-article. Please also note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.  \n© 2025 Published by Elsevier Inc.  \nMachine learning to improve analysis of electronic health data on disability and health: an untapped opportunity for health inequities research  \nSara Rotenberg, DPhil,1* Munib Mesinovic, MSc2, Eirini-Christina Saloniki, PhD3, Shanquan Chen, PhD1, Rosalind Raine, PhD4 , Hannah Kuper, ScD1  \n1 International Centre for Evidence in Disability, London School of Hygiene and Tropical Medicine, London, UK  \n2 Department of Engineering Science, University of Oxford, Oxford, UK  \n3 Global Business School for Health, University College London (UCL), London, UK  \n4 Department of Primary Care and Population Health, University College London (UCL), London, UK  \n*corresponding author: Dr. Sara Rotenberg ([sara.rotenberg@lshtm.ac.uk](sara.rotenberg@lshtm.ac.uk)) Abstract word count: 147  \nManuscript word count: 2961  \nReferences: 34 Funding Statement  \nHK and SR are funded by HK's NIHR Global Research Professorship (NIHR301621) . SR and SC are funded by the PENDA project from the Foreign, Commonwealth and Development Office. MM is funded by the Rhodes Trust. ECS and RR are funded by the National Institute for Health and Care Research Applied Research Collaboration North Thames. The views expressed in this publication are those of the authors and not necessarily those of the National Institute for Health and Care Research or the Department of Health and Social Care.  \nAuthors’ contributions statement  \nSR designed the outline of the paper and wrote the first draft. MM, ECS, and SC wrote specific sections or provided specific case study examples. All authors contributed to the writing and reviewed and agreed the final version.  \n1 Abstract (147 words)  \n2 Electronic Health Records (EHRs) are a leading source of epidemiological data, but often lack  \n3 standardised disability information. This gap hampers our ability to analyse the full scope of health  \n4 inequities faced by people with disabilities. Current approaches to identify disability within EHRs  \n5 have limitations because of inadequate proxies for disability or issues linking data sources. Machine  \n6 learning (ML) offer unprecedented opportu","cbCaigmfEv6cn4hF","https://ap.wps.com/l/cbCaigmfEv6cn4hF","pdf",789506,12,"English","# Abstract\n# Background\n## Disability prevalence and health inequities\n## Limits of existing data and linkage restrictions\n# Machine learning opportunities in EHRs\n## Disability markers and classification\n## Natural language processing for clinical notes\n# Ethical considerations and validation","[{\"question\":\"为什么电子健康记录中的残障与健康信息常常不足？\",\"answer\":\"EHRs往往缺少标准化的残障信息，导致无法分析残障人群面临的健康不平等全貌。现有做法也受制于残障代理指标不足以及数据源关联问题。\"},{\"question\":\"文中提出如何利用机器学习生成残障标记？\",\"answer\":\"文中指出可以在EHR中构建残障标记，例如用无监督学习对残障群体进行分类，并通过自然语言处理从临床记录中提取相关信息。\"},{\"question\":\"这些方法对健康不平等研究有什么潜在价值？\",\"answer\":\"改进EHR中的残障数据有助于开展更充分的残障分层分析，揭示照护路径与结局的模式，从而提升对健康不平等的理解。文中同时强调需要遵循伦理规范并对新方法进行验证。\"}]","Machine learning to improve analysis of electronic health data on disability and health - an untapped opportunity for health inequities research | PDF"]