Unstructured Electronic Health Records of Dysphagic Patients Analyzed by Large Language Models.

ABSTRACT:

Objective

Dysphagia is a common and complex disorder that complicates both diagnoses and treatment. Consequently, the associated electronic health records (EHR) are often unstructured and complex, posing challenges for systematic data analysis.

Methods and procedures

In this study, we employ natural language processing (NLP) techniques and large language models (LLMs) to automatically analyze clinical narratives and extract diagnostic information from a diverse set of EHRs. Our dataset includes medical records from 486 patients, representing a group with diverse dysphagic conditions. We analyze diagnoses provided in unstructured free text that do not follow a standardized structure. We utilize clustering algorithms on the extracted diagnostic features to identify distinct gr

SUBMITTER: Neubig L 

PROVIDER: S-EPMC12310174 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature