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

Clinical Information and Decision Making (1960) Generative Language Models Including ChatGPT (745) AI Language Models in Health Care (215) Machine Learning (2134) Clinical Decision Support for Anesthesiology (14) Perioperative Risk Assessment (30)

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 (2) Tweetations Metrics Author Video

Journals

  1. Hao X, Wang Y, Li K, Zhu T, Herasevich V. Applying machine learning for perioperative adverse event prediction: a narrative review toward better clinical efficacy and usability. Anesthesiology and Perioperative Science 2025;3(4) View
  2. Damascena T, Marques R, Matos F, Assumpção C, Silva R, Nascimento A, Moreira G, Passos M, Santos L, Arruda S. CUIDADO PRÉ-OPERATÓRIO CENTRADO EM RISCO: PROTOCOLO MULTIPROFISSIONAL E COMPLICAÇÕES PÓS-CIRÚRGICAS. Cognitus Interdisciplinary Journal 2026;3(1):71 View