Serializing all patient data — notes, vitals, labs — into one text sequence and fine-tuning an LLM matches or beats task-specific multimodal fusion baselines on mortality, graft-failure, and triage prediction.
Shebesh, Jing Su, Paul R
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Large Language Models as Unified Multimodal Learners for Clinical Prediction
Serializing all patient data — notes, vitals, labs — into one text sequence and fine-tuning an LLM matches or beats task-specific multimodal fusion baselines on mortality, graft-failure, and triage prediction.