Innovations in otolaryngology using LLM for early detection of sleep-disordered breathing

作者：Jin Zhou, Xiaoqin Li, Qiujie Xia, Liang Yu · 发表于：SLAS TECHNOLOGY · 年份：2025 · DOI：10.1016/j.slast.2025.100278 · 被引用次数：6 · 研究领域：Obstructive Sleep Apnea Research、Voice and Speech Disorders、Dysphagia Assessment and Management

Sleep Disordered Breathing (SDB), including conditions like Obstructive Sleep Apnea (OSA), represents a major health concern, characterized by irregular airflow during sleep due to airway obstruction. SDB can result in serious health problems. Implementation of early intervention is vital whenever patient outcomes are to be considered. This research aims to advance research on otolaryngology using Machine Learning (ML) models, and Large Language Models (LLM) for identification of SDB using Electronic Health Record (HER). The approach proposes a hybrid ML framework combining the Dynamic Seagull Search algorithm-driven Large Language model (DSS-LLM). The extensive clinical dataset is used to train the model. It includes patient demographics, medical history, sleep habits, comorbidities, and physical measurements. Data pre-processing involves handling missing values, applying NLP techniques, and normalization. Feature extraction is done using Principal Component Analysis (PCA) to reduce the dimensionality of the hyperparameters and finally for selecting the best set of predictors. The extracted features are then used to train the proposed DSS-LLM model, which incorporates the DSS algorithm to optimize the LLM classifier, improving classification accuracy and model robustness. Subsequently, the idea of LLM is introduced for its application on textual clinical records comprising physicians' reports and patients' symptoms. The findings from an experiment suggest that the proposed m...