This paper is in the following e-collection/theme issue:

Clinical Information and Decision Making (1935) Generative Language Models Including ChatGPT (725) AI Language Models in Health Care (208) Machine Learning (2095) Clinical Decision Support for Anesthesiology (14) Perioperative Risk Assessment (29)

Published on in Vol 27 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/75052, first published .
Predicting 30-Day Postoperative Mortality and American Society of Anesthesiologists Physical Status Using Retrieval-Augmented Large Language Models: Development and Validation Study

Predicting 30-Day Postoperative Mortality and American Society of Anesthesiologists Physical Status Using Retrieval-Augmented Large Language Models: Development and Validation Study

Predicting 30-Day Postoperative Mortality and American Society of Anesthesiologists Physical Status Using Retrieval-Augmented Large Language Models: Development and Validation Study

Authors of this article:

Ying-Hao Chen1 Author Orcid Image ;   Shanq-Jang Ruan1 Author Orcid Image ;   Pei-fu Chen2, 3 Author Orcid Image
Article Authors Cited by (1) Tweetations Metrics Author Video

Ying-Hao Chen   1 , BS ;   Shanq-Jang Ruan   1 , PhD ;   Pei-fu Chen   2, 3 , MD, PhD

1 Department of Electronic and Computer Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan

2 Department of Anesthesiology, Far Eastern Memorial Hospital, New Taipei City, Taiwan

3 Department of Electrical Engineering, Yuan Ze University, Taoyuan, Taiwan

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