Perioperative Risk Assessment to Predict Hemodynamic Instability in Cardiac Surgery Patients

This project aims to enhance the prediction of hemodynamic instability in cardiac surgery patients undergoing cardiopulmonary bypass, addressing the critical challenge of perioperative instability that can lead to severe complications and increased healthcare utilization. The project integrates comprehensive preoperative, intraoperative, and postoperative data, focusing on dynamic changes during surgery often overlooked by current models. A novel method was developed to extract stiffness indices from time-series pulmonary arterial pressure data, transforming raw measurements into predictive inputs. Machine learning models were implemented and optimized through extensive hyperparameter tuning and cross-validation for classification tasks. Correlation matrices focused on the target variable to determine feature importance, ensuring that the most predictive elements were utilized to forecast patient outcomes effectively. This comprehensive approach improves predictions of postoperative outcomes and offers real-time insights that enhance patient management.