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

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.