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.
Authors
- Y. Chen
- Shanq-Jang Ruan (ORCID: https://orcid.org/0000-0003-1075-8512)
- Pei‐Fu Chen (ORCID: https://orcid.org/0000-0002-0192-5377)
Institutions
- National Taiwan University of Science and Technology (TW)
- Far Eastern Memorial Hospital (TW)
- Yuan Ze University (TW)
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