A large language model based pipeline for extracting information from patient complaint and anamnesis in clinical notes for severity assessment.

ABSTRACT: Identifying patients with critical illness in emergency departments (EDs) is an ongoing challenge, partly due to the limited information available at the time of admission. The clinical notes in patient records have already received attention for the value of improving prediction. Recent large language models (LLMs) have demonstrated their promising performance. However, the utilization of LLMs for analyzing clinical notes has not been extensively investigated. To improve the severity assessment of illness and the prediction of triage level, we developed a pipeline for utilizing LLMs (e.g. ChatGLM-2, GLM-4 and Alpaca-2) to extract information from patient complaint and anamnesis in clinical notes. In this pipeline, a LLM is supplied with the text input including complaint and anamnesis of

SUBMITTER: Gao H 

PROVIDER: S-EPMC12260085 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature