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<p>Multiple long-term conditions (MLTCs), or multimorbidity—the co-occurrence of multiple chronic conditions—present a growing challenge for primary care. Existing predictive models typically focus on single outcomes and often fail to capture the temporal and competing-risk structure inherent in longitudinal electronic health records (EHRs). Here, we present SurvivEHR, a generative transformer-based foundation model trained on over 7.6 billion coded events from 23 million patients in UK primary care. SurvivEHR is pre-trained using a competing-risk, time-to-next-event objective, enabling calibrated risk stratification across a broad range of diagnoses, investigations, medications, and mortality events. We show that this pre-training objective yields strong next-event discrimination and learns clinically meaningful patient trajectories. When adapted through fine-tuning, SurvivEHR achieves improved performance on downstream prognostic tasks, including longer-horizon risk prediction, with particular benefits in low-resource settings. By learning longitudinal patient representations directly from routine primary care records, SurvivEHR provides a scalable foundation for developing generalisable clinical risk models that reflect the complexity of MLTCs in primary care.</p>

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SurvivEHR: a competing risks, time-to-event foundation model for multiple long-term conditions from primary care electronic health records

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月9日

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  • Charles Gadd,
  • Krishna Gokhale,
  • Aditya Acharya,
  • Jennifer Cooper,
  • Francesca Crowe,
  • Leah Fitzsimmons,
  • Thomas Jackson,
  • Krishnarajah Nirantharakumar,
  • Christopher Yau,
  • Rebecca Birch,
  • Marco Canducci,
  • Dominic Danks,
  • Alexander d’Elia,
  • Alastair Denniston,
  • Sarah Flanagan,
  • Suzy Gallier,
  • Naijie Guan,
  • Xin Guan,
  • Imane Guellil,
  • Georgios Gkoutos,
  • Shamil Haroon,
  • Eleanor Hathaway,
  • Louise Jackson,
  • Janet Lord,
  • Zeinab Majid,
  • Tom Marshall,
  • George Morris,
  • Charlotte Owen,
  • Elizabeth Sapey,
  • Chris Sainsbury,
  • Charlotte Spurway,
  • Peter Tino,
  • Steven Wambua,
  • Amaya Azcoaga-Lorenzo,
  • Colin McCowan,
  • Luciana Rocha Pedro,
  • Muhammad Usman,
  • Natalia Hong,
  • Sara Matijevic,
  • Kaspar Martens,
  • Tim Williams,
  • Puja Myles

摘要

Multiple long-term conditions (MLTCs), or multimorbidity—the co-occurrence of multiple chronic conditions—present a growing challenge for primary care. Existing predictive models typically focus on single outcomes and often fail to capture the temporal and competing-risk structure inherent in longitudinal electronic health records (EHRs). Here, we present SurvivEHR, a generative transformer-based foundation model trained on over 7.6 billion coded events from 23 million patients in UK primary care. SurvivEHR is pre-trained using a competing-risk, time-to-next-event objective, enabling calibrated risk stratification across a broad range of diagnoses, investigations, medications, and mortality events. We show that this pre-training objective yields strong next-event discrimination and learns clinically meaningful patient trajectories. When adapted through fine-tuning, SurvivEHR achieves improved performance on downstream prognostic tasks, including longer-horizon risk prediction, with particular benefits in low-resource settings. By learning longitudinal patient representations directly from routine primary care records, SurvivEHR provides a scalable foundation for developing generalisable clinical risk models that reflect the complexity of MLTCs in primary care.