A non-linear time series based artificial intelligence model to predict outcome in cardiac surgery

摘要

Background Adverse lifestyles have led to increased cardiac complications, further accelerating the burden of cardiac surgeries in tertiary care hospitals. For optimum management of cardiac surgical patients in the hospital, it is essential to have an accurate idea regarding the patients' expected ICU stay and hospital stay. Additionally, forecasting patients' survival outcome is also essential for ICU management. Objectives This study aims to develop artificial intelligence models based on non-linear time-series data of blood pressure and heart rate to predict the ICU stay, hospital stay, and survival outcome of cardiac surgical patients. Methods The intraoperative heart rate and blood pressure data of 6064 patients undergoing cardiac surgeries at a single tertiary care hospital were recorded every minute. After data cleaning, the data was split into 781 patients in the train data set and 296 patients in the test data set. Feature engineering and balancing of data were performed on the train data set. Various classification models for survival outcome and regression models for ICU stay and hospital stay were trained using the balanced train data set. These models were tested on the test data set, and the prediction results were evaluated on the following performance metrics: area under the curve (AUC), accuracy, F1-score, RMSE, and R2-score. Results The Gaussian Naive Bayes + Logistic Regression (GNB + LR) model is the best for survival analysis, having the highest AUC of 0.72, Accuracy of 83, and an F1-score of 0.86. The Gradient boosting (GB) model is the best model for the analysis of hospital stay, offering the highest R2-score (0.023). The XGBoost regressor is the best model for ICU stay analysis, offering the highest R2-score (0.125). Conclusion Artificial intelligence models based upon the intraoperative time series data were developed to analyse outcomes in cardiac surgery with high accuracy. These models can be used in cardiac surgeries to predict the ICU stay, hospital stay, and overall survival of the patients for better ICU management at the hospital.