Classifying American Society of Anesthesiologists Physical Status With a Low-Rank-Adapted Large Language Model: Development and Validation Study.

backgroundThe American Society of Anesthesiologists Physical Status (ASA-PS) classification is integral to preoperative risk assessment; yet, assignment remains subjective and labor-intensive. Recent large language models (LLMs) process free-text electronic health records (EHRs), but few studies have evaluated parameter-efficient adaptations that both predict ASA-PS and provide clinician-readable rationales. Low-rank adaptation (LoRA) is a parameter-efficient technique that updates only a small set of add-on parameters rather than the entire model, enabling efficient fine-tuning on modest data and hardware. A lightweight, instruction-tuned LLM with these capabilities could streamline workflow and broaden access to explainable decision support.

objectiveThis study aimed to develop and evaluate a LoRA-fine-tuned large language model meta-AI (LLaMA-3) for ASA-PS classification from preoperative clinical narratives and benchmark it against traditional machine learning classifiers and domain-specific LLMs.

methodsPreoperative anesthesia notes and discharge summaries were extracted from the EHR and reformatted into an Alpaca-style instruction-response prompt, requesting ASA-PS class labels (I-V) annotated by anesthesiologists. The LoRA-enhanced LLaMA-3 model was fine-tuned with mixed-precision training and evaluated on a hold-out test set. Baselines included random forest classifier, Extreme Gradient Boosting (XGBoost) classifier, support vector machine, fastText, BioBERT, ClinicalBERT, and untuned LLaMA-3. Performance was assessed with micro- and macroaveraged F

resultsThe LoRA-LLaMA-3 model achieved a micro-F

conclusionsLoRA fine-tuning improved LLaMA-3 from near-random performance into an ASA-PS classifier with higher micro-F