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# SPARC transformer model uses routine measurements to predict cancer adverse events
- URL: https://www.coios.me/sparc-transformer-model-uses-routine-measurements-to/
- Published: 2026-10-31T11:15:59.000Z
- Updated: 2026-10-31T11:50:00.000Z
- Description: A transformer model trained on 224,047 patients' routine measurements predicted cancer adverse events in prospective and external cohorts.
- Author: Daniel Ryan
- Tags: Data, models and systems, AI in medicine, Cancer, #preprint, #new, #item, #source-2026-10, #score-76

SPARC, a transformer-based foundation model, was trained on 101,920,379 routine measurements from 224,047 patients with cancer to predict adverse events. Testing used held-out data, a prospective cohort of 23,126 and public datasets covering 26,012 patients. External multi-hospital cohorts of 562,620 and 206,840 patients were also used. The authors report state-of-the-art performance, and SPARC identified patients most likely to benefit from G-CSF. Fine-tuning on pooled data from several hospitals generalised better than fine-tuning on one. The abstract gives no accuracy figures or comparators, and shows no change in patient outcomes.

*Why it is interesting: The model was validated prospectively and in external multi-hospital cohorts of 562,620 and 206,840 patients. It also flagged who might benefit from G-CSF, a named intervention, rather than only scoring risk.*

Source

[medRxiv, 29 October 2026](https://doi.org/10.64898/2026.10.22.26364879?ref=coios.me)

Paper

Longitudinal AI Models Predict Cancer Adverse Events

Authors

Fu C, Yu J, Shi R, Waters M, Pavlak M, Arfe A et al.

DOI

10.64898/2026.10.22.26364879

Type

Preprint

Design

Transformer foundation model (SPARC) pre-trained and fine-tuned on 101,920,379 routine measurements from 224,047 cancer patients; held-out, prospective (n=23,126) and external validation (n=26,012; 562,620; 206,840)

Verdict

New finding

Driver

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

Driver

[Cancer](https://www.coios.me/d-cancer/)