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

The LLaMA-RAG model significantly improved the prediction of postoperative mortality and ASA classification, especially for rare high-risk cases. By grounding outputs in domain knowledge, retrieval-augmented generation enhanced both accuracy and prompt‑driven interpretability over ML and ablation models-highlighting its promise for real-world clinical decision support.

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Publication Details

Journal
Journal of Medical Internet Research
Published
2025-06-03
DOI
https://doi.org/10.2196/75052
Citations
1
Primary Topic
Cardiac, Anesthesia and Surgical Outcomes
Type
article
Field-Weighted Citation Impact
1.39
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article

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

Y. Chen, Shanq-Jang Ruan, Pei‐Fu Chen
1 citations
Journal of Medical Internet Research
Cardiac, Anesthesia and Surgical Outcomes
1.39
article

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

Y. Chen, Shanq-Jang Ruan, Pei‐Fu Chen
article en
1 citations

Abstract

The LLaMA-RAG model significantly improved the prediction of postoperative mortality and ASA classification, especially for rare high-risk cases. By grounding outputs in domain knowledge, retrieval-augmented generation enhanced both accuracy and prompt‑driven interpretability over ML and ablation models-highlighting its promise for real-world clinical decision support.

Journal of Medical Internet ResearchVol. 27
National Taiwan University of Science and Technology (TW), Far Eastern Memorial Hospital (TW), Yuan Ze University (TW)
Openalex Percentile: Top 18%
Cardiac, Anesthesia and Surgical Outcomes
1.39
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