Chenlian Fu

Predicting cancer treatment side effects has long relied on static nomograms built from a handful of variables. SPARC instead models longitudinal sequences of blood counts, metabolic panels and vital signs, pretraining on 101,920,379 measurements from 224,047 patients and then fine-tuning for mortality, anemia, neutropenia, febrile neutropenia, acute kidney injury and liver toxicity. It matched or led comparison models on held-out, prospective and multi-hospital cohorts, and its predicted risk strata identified patients most likely to benefit from G-CSF prophylaxis.

Predicting cancer treatment side effects has long relied on static nomograms built from a handful of variables. SPARC instead models longitudinal sequences of blood counts, metabolic panels and vital signs, pretraining on 101,920,379 measurements from 224,047 patients and then fine-tuning for mortality, anemia, neutropenia, febrile neutropenia, acute kidney injury and liver toxicity. It matched or led comparison models on held-out, prospective and multi-hospital cohorts, and its predicted risk strata identified patients most likely to benefit from G-CSF prophylaxis.