Retrieval-Based Language Model Framework for Predicting Postoperative Complications Under Class Imbalance

Accurate prediction of postoperative complications, such as acute myocardial injury (AMI) and acute kidney injury (AKI), is essential for informed clinical decision-making and improved patient outcomes. However, conventional machine learning approaches often exhibit degraded performance in this setting due to severe class imbalance and the need for extensive feature preprocessing. To address these challenges, we propose a retrieval-based disease prediction (RBD) framework that leverages language models for postoperative risk assessment. The proposed framework converts heterogeneous preoperative and intraoperative clinical data into textual representations and retrieves relevant disease information by comparing patient-specific descriptions with predefined disease definitions of AMI and AKI. This retrieval-based formulation reduces the dependence on complex data normalization and resampling strategies commonly required by traditional models. Experimental results demonstrate that the RBD framework consistently outperforms existing machine learning methods in predicting postoperative complications under imbalanced data conditions. These findings indicate that retrieval-based language model analytics provide a promising approach for clinical decision support in postoperative care.