> ## Content Index
> Fetch the complete content index at: https://www.coios.me/llms.txt
> Use this file to discover other available public pages before exploring further.

# A generative model of patient records stratifies five-year risk of first cancer
- URL: https://www.coios.me/a-generative-model-of-patient-records-stratifies-five-year/
- Published: 2026-09-20T09:34:46.000Z
- Updated: 2026-09-20T09:34:46.000Z
- Description: A preprint describes an autoregressive generative model trained on electronic health records from millions of patients, reporting that supervised adaptation improved prediction of a first cancer diagnosis within five years across five cohorts.
- Author: Daniel Ryan
- Tags: Emergent technology, AI in medicine, #preprint, #new, #item, #source-2026-09

The authors trained an autoregressive generative model on longitudinal electronic health records from millions of patients, explicitly representing the irregular time gaps between encounters, then adapted it with parameter-efficient supervised fine-tuning for pan-cancer risk stratification. Across five large EHR cohorts, the adapted model improved prediction of a first cancer diagnosis within a five-year window relative to the foundational representation alone; no absolute discrimination figures are given in the abstract. The authors frame the work as retrospective evidence supporting prospective evaluation for prioritising patients for risk-based screening, including pancreatic and ovarian cancer.

*Why it is interesting: Tests whether a general-purpose generative model of patient histories, rather than a task-specific risk equation, can identify who to screen, so far only retrospectively.*

Source

[medRxiv, 19 September 2026](https://doi.org/10.64898/2026.09.09.26362676?ref=coios.me)

DOI

10.64898/2026.09.09.26362676

Type

Preprint

Design

Retrospective development and validation of a generative EHR foundation model (GenEHR) with supervised adaptation, five large EHR cohorts, millions of patients, five-year horizon

Verdict

New finding

Driver

[AI in medicine](https://www.coios.me/d-ai-medicine/)