Abstract
Clinical interventions for cancer treatment toxicities can reduce morbidity, mortality, and healthcare cost, if high risk patients can be identified. We developed SPARC, a transformer-based foundation model, using real-world, longitudinal, routinely collected health measurements, to predict adverse event risk. Pre-training and fine-tuning SPARC on 101,920,379 measurements from 224,047 patients with cancer led to state-of-the-art performance at predicting cancer-related adverse events on (i) held-out data from the development cohort, (ii) a prospective validation cohort from the same center (Npatients=23,126), (iii) multiple publicly available external datasets (Npatients=26,012), and (iv) external, multi-hospital cohorts (Npatients=562,620, 206,840). SPARC generalized to multiple external centers and identified patients most likely to benefit from an intervention (G-CSF). Pooled multi-institution, more than single-institution, fine-tuning improved generalizability. We release SPARC as a community resource. Our results highlight the utility of AI time series models in oncologic management.
Presented at ISMB 2026 (Best Oral Presentation).
Jennifer Yu
PhD Student in Computational Biology and Medicine
My research interests include machine learning, large language models, precision oncology and clinical time series.