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Comparison of NLP machine learning models with human physicians for ASA Physical Status classification

Medicine and Health

Comparison of NLP machine learning models with human physicians for ASA Physical Status classification

S. B. Yoon, J. Lee, et al.

This groundbreaking study explores the potential of natural language processing (NLP) in automating ASA-PS classification, achieving superior performance compared to human anesthesiologists. Conducted by Soo Bin Yoon and colleagues, the ClinicalBigBird model showcased an impressive AUROC of 0.915, indicating a transformative step towards streamlined clinical workflows.

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Playback language: English
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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