CNSight LLM Pipeline for Clinical Note Sections
Figure 1: LLMs enable automated segmentation of clinical notes. Overview of the CNSight pipeline, where free-text or sentence-split clinical notes are processed by large language models and baseline methods to extract structured clinical sections such as patient history, chief complaint, medications, and discharge instructions.
Paper context
Paper title: CNSight: Evaluation of Clinical Note Segmentation Tools Abstract: Clinical notes are often stored in unstructured or semi-structured formats after extraction from electronic medical record (EMR) systems, which complicates their use for secondary analysis and downstream clinical applications. Reliable identification of section boundaries is a key step toward structuring these notes, as sections such as history of present illness, medications, and discharge instructions each provide distinct clinical contexts. In this work, we evaluate rule-based baselines, domain-specific transformer models, and large language models for clinical note segmentation using a curated dataset of 1,000 notes from MIMIC-IV. Our experiments show that large API-based models achieve the best overall performance, with GPT-5-mini reaching a best average F1 of 72.4 across sentence-level and freetext segmentation. Lightweight baselines remain competitive on structured sentence-level tasks but falter on unstructured freetext. Our results provide guidance for method selection and lay the groundwork for downstream tasks such as information extraction, cohort identification, and automated summarization. Passages referencing this figure: Figure 1: LLMs enable automated segmentation of clinical notes.
