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Abstract
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. The ClinicalBigBird model achieved an area under the receiver operating characteristic curve (AUROC) of 0.915, outperforming board-certified anesthesiologists in specificity, precision, and F1-score. This suggests an NLP-based approach can automate ASA-PS classification, streamlining clinical workflows.
Publisher
npj Digital Medicine
Published On
Sep 28, 2024
Authors
Soo Bin Yoon, Jipyeong Lee, Hyung-Chul Lee, Chul-Woo Jung, Hyeonhoon Lee
Tags
Natural Language Processing
ASA-PS Classification
ClinicalBigBird
Anesthesia Evaluation
Machine Learning
Automation
Healthcare
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