Enhancing large language model clinical support information with machine learning risk and explainability: a feasibility study

Abstract Background Current machine learning (ML) prediction models offer limited guidance for individualized actionable management. Large language models (LLMs) can transform ML model-predicted risk estimates with Shapley Additive Explanations (SHAP) into clinically meaningful support information,...

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Principais autores:	Yu-Chang Yeh, Hsin-Yu Yang, Ching-Tang Chiu, Anne Chao, Yu-Chen Chuang, Wing-Sum Chan
Formato:	Artigo
Idioma:	Inglês
Publicado:	SpringerOpen 2026-04-01
Series:	Intensive Care Medicine Experimental
Assuntos:	Critical care Machine learning Large language model Generative artificial intelligence Clinical support system
Acceso en liña:	https://doi.org/10.1186/s40635-026-00900-w
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