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Longitudinal AI Models Predict Cancer Adverse Events
SPARC, a transformer-based foundation model trained on more than 100 million routinely collected longitudinal clinical measurements from 224,047 patients with cancer, predicts treatment-related adverse events and generalizes to external centers.
Chenlian Fu, Jennifer Yu, Ruian Shi, Michele Waters, Mitchell Pavlak, Andrea Arfe, Sohrab P. Shah, Alexis Battle, Kenneth L. Kehl, Brian M. Bot, US Cancer AI Alliance (CAIA), Nikolaus Schultz, Quaid Morris, Justin Jee
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Generalist large language models complement tailor-made predictors for tumor genomics interpretation
General-purpose large language models, used without any fine-tuning, match or complement specialized predictors on three tumor genomic interpretation tasks, with the largest gains on data from new assays and institutions.
Jennifer Yu, Madison Darmofal, Michele Waters, January Choy, Thinh N. Tran, Chenlian Fu, Leah Morales, Kaicheng U, Ross L. Levine, Nikolaus Schultz, Michael F. Berger, Quaid Morris, Justin Jee
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CodeWearBook: Retrieval-Grounded Natural-Language Querying of Wearable Health Datasets Using Large Language Models
CodeWearBook is a retrieval-grounded large language model agent that answers natural-language questions about wearable health datasets through controlled SQL and Python execution layers, so the model’s access to raw data is restricted and the workflow can be adapted to new data sources and contexts.
Phoebe Chuang, Tina Behrouzi, Jennifer Yu, Robin Yang, K. Stemmler, Sarah M. Goodday, Stephen H. Friend, Anna Goldenberg
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