PREBA: Surgical Duration Prediction via PCA-Weighted Retrieval-Augmented LLMs and Bayesian Averaging Aggregation

Researchers developed PREBA, a retrieval-augmented framework that uses PCA-weighted retrieval and Bayesian averaging to improve surgical duration prediction accuracy by up to 40% using large language models. The system grounds LLM predictions in institution-specific clinical data without requiring computationally intensive training, achieving performance competitive with supervised machine learning methods.