MILM fine-tunes LLMs on XML-encoded multimodal irregular time series via a two-stage process that exploits informative sampling patterns to achieve top performance on EHR classification datasets.
MIMIC-IV- Note: Deidentified free-text clinical notes.PhysioNet, January 2023
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An agentic LLM reasoning system reached 79.6% agreement with expert consensus on myeloma care questions from longitudinal records, outperforming iterative RAG and full-context baselines by 3.8-4.2 points with larger gains on complex cases.
Single-agent LLM frameworks outperform naive multi-agent systems in multimodal clinical risk prediction tasks and are better calibrated.
citing papers explorer
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MILM: Large Language Models for Multimodal Irregular Time Series with Informative Sampling
MILM fine-tunes LLMs on XML-encoded multimodal irregular time series via a two-stage process that exploits informative sampling patterns to achieve top performance on EHR classification datasets.
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Agentic clinical reasoning over longitudinal myeloma records: a retrospective evaluation against expert consensus
An agentic LLM reasoning system reached 79.6% agreement with expert consensus on myeloma care questions from longitudinal records, outperforming iterative RAG and full-context baselines by 3.8-4.2 points with larger gains on complex cases.
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AgentRx: A Benchmark Study of LLM Agents for Multimodal Clinical Prediction Tasks
Single-agent LLM frameworks outperform naive multi-agent systems in multimodal clinical risk prediction tasks and are better calibrated.