Journal Article

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

Fitzsimmons, Leah, Acharya, Aditya, Yau, Christopher, Jackson, Thomas, Nirantharakumar, Krishnarajah, Cooper, Jennifer, Crowe, Francesca, Gadd, Charles, Gokhale, Krishna

2026

Overview

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.

• Publisher: Nature Publishing Group UK,Nature Publishing Group,Nature Portfolio
• Subject: 631/114 / 639/705 / 692/308 / 692/699 / 692/700 / Biomedicine / Biotechnology / Chronic illnesses / Deep learning / Electronic health records / Health care / Medicine / Medicine & Public Health / Patient assessment / Primary care