Mortality prediction for ICU patients with mental disorders using large language models ensemble and unstructured medical notes.

ABSTRACT: Assessing mortality risk in the intensive care unit (ICU) is crucial for improving clinical outcomes and management strategies. Conventional artificial intelligence studies often neglect vital clinical information contained in unstructured medical notes. Recently, large language models (LLMs) have achieved leading-edge performance in natural language processing tasks, though each model has limitations stemming from its architecture and pre-training. The ensemble of heterogeneous language models, including both conventional LMs and LLMs, effectively addresses these constraints. The study introduces a predictive ensemble classifier using a decision fusion approach of diverse medical LLMs and LMs, including Asclepius, Meditron, GatorTron, and PubMedBERT. These models, fine-tuned with multimod

SUBMITTER: Nazih W 

PROVIDER: S-EPMC12448993 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature