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

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.

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 groups of patients who share similar pathophysiological swallowing dysfunctions.

We found that basic NLP techniques often provide limited insights due to the high variability of the data. In contrast, LLMs help to bridge the gap in understanding the nuanced medical information about dysphagia and related conditions. Although applying these advanced LLM models is not straightforward, our results demonstrate that leveraging closed-source models can effectively cluster different categories of dysphagia.

Our study provides therefore evidence that LLMs are highly promising in future dysphagia research.

Dysphagia is a symptom associated with various diseases, though its underlying relationships remain unclear. This study demonstrates how analyzing large volumes of electronic health records can help clarify the causes of dysphagia and identify contributing factors. By applying natural language processing, we aim to enhance both understanding and treatment, supporting clinical staff in improving individualized care by identifying relevant patient cohorts. Clinical and Translational Impact Statement: This study uses LLMs to efficiently preprocess unstructured EHRs, improving dysphagia diagnosis and patient clustering. It aligns with Clinical Research, enhancing diagnostic speed and enabling personalized treatment.