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

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.

Pixel-Based Swallow Measurements: Correcting Nonsquare Pixels.

Purpose: This research note illustrates the effects of video data with nonsquare pixels on the pixel-based measures obtained from videofluoroscopic swallow studies (VFSS).

Method: Six pixel-based distance and area measures were obtained from two different videoflouroscopic study units; both yielding videos with nonsquare pixels with different pixel aspect ratios (PARs). The swallowing measures were obtained from the original VFSS videos and from the videos after their pixels were squared.