P4MC: A Monte Carlo Classification Method Based on Partial Variables for Postoperative Complication Prediction

Predicting postoperative complications is a critical task for improving patient care and optimizing resource management. We formulate the task as a binary classification problem based on two-stage clinical data: preoperative and intraoperative variables. A key difficulty is that intraoperative data are systematically missing for preoperative prediction. Existing methods, which typically rely solely on preoperative data or single intraoperative indicator imputation, generally fail to capture important latent intraoperative information. To provide a more robust solution, we propose P4MC (Preoperative, Partial intraoPerative, and Postoperative Monte Carlo classification). This approach explicitly models the dependence between the two stages to recover latent intraoperative information. Specifically, P4MC first applies partial sufficient dimension reduction (PSDR) to compress intraoperative variables into a low-dimensional sufficient reduction. We then estimate the conditional distribution of this reduction given preoperative variables using nonparametric density estimation and generate latent intraoperative features for classification via Monte Carlo sampling. A simulation study demonstrates the improved predictive performance and stability of P4MC when combined with standard machine learning methods. Applications to two clinical cohorts, laparoscopic pancreaticoduodenectomy and hepatocellular carcinoma, further show that P4MC outperforms conventional approaches and yields clinically interpretable insights. An open-source R program is provided in the P4MC package, available at https://github.com/501fox/P4MC.git.