인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
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 model enhances the classification accuracy achieved to 98.91 %, precision attained by 98.9 %, recall achieved to 98.92 % and F-1 score attained by 98.58 % as compared to the models developed earlier. This research provides a novel solution to the screening of OSA at the pre-clinical level which involves hybrid machine learning models integrated with LLMs. This proposed framework is expected to boost clinical judgment and thereby increase better ophthalmology outcomes for patients.
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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