한빛사논문
- 조회122
Soo Bin Yoon1, Jipyeong Lee2, Hyung-Chul Lee1,3, Chul-Woo Jung1 & Hyeonhoon Lee2,3
1Department of Anesthesiology and Pain Medicine, Seoul National University College of Medicine, Seoul National University Hospital, Seoul, Republic of Korea.
2Department of Anesthesiology and Pain Medicine, Seoul National University Hospital, Seoul, Republic of Korea.
3Department of Data Science Research, Innovative Medical Technology Research Institute, Seoul National University Hospital, Seoul, Republic of Korea.
Corresponding author
Correspondence to Hyeonhoon Lee.
Abstract
The American Society of Anesthesiologist’s Physical Status (ASA-PS) classification system assesses comorbidities before sedation and analgesia, but inconsistencies among raters have hindered its objective use. This study aimed to develop natural language processing (NLP) models to classify ASA-PS using pre-anesthesia evaluation summaries, comparing their performance to human physicians. Data from 717,389 surgical cases in a tertiary hospital (October 2004–May 2023) was split into training, tuning, and test datasets. Board-certified anesthesiologists created reference labels for tuning and test datasets. The NLP models, including ClinicalBigBird, BioClinicalBERT, and Generative Pretrained Transformer 4, were validated against anesthesiologists. The ClinicalBigBird model achieved an area under the receiver operating characteristic curve of 0.915. It outperformed board-certified anesthesiologists with a specificity of 0.901 vs. 0.897, precision of 0.732 vs. 0.715, and F1-score of 0.716 vs. 0.713 (all p <0.01). This approach will facilitate automatic and objective ASA-PS classification, thereby streamlining the clinical workflow.
논문정보
- Research article
- 2024년 09월 (BRIC 등록일 2024-09-30)
- 국내 연구진
- 바이오·의료융합 > 의료기기
댓글 작성 로그인
팝업 닫기관련 링크
연구자 키워드
관련분야 연구자보기
소속기관 논문보기
관련분야 논문보기
해당논문 저자보기
