This study introduces a novel clinical AI framework integrating Large Language Models (LLMs) with Retrieval-Augmented Generation (RAG) to predict 30-day postoperative mortality and ASA physical status classifications based on unstructured preoperative clinical notes.
We conducted a retrospective cohort study using 24,491 medical records from a tertiary medical center.
To extract clinical insights from free-text data, we used the LLaMA 3.1-8B language model with RAG, using MedEmbed for text embedding and Miller's Anesthesia as the primary retrieval source.
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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