Measures of Entropy and Change Point Analysis as Predictors of Post-Surgical Adverse Outcomes
A variety of adverse outcomes, such as kidney injury, death, cardiac injury, and respiratory failure affect a significant number of patients after surgery. Previous research has investigated possible predictors for these outcomes including features extracted from physiologic time series. This study builds upon this previous work by exploring entropy, long-term memory, and change point analysis as different and possibly predictive measures of volatility. To do this, we use both random forest models and the robust method of L1 regularized logistic regression as modeling frameworks for the prediction. Predictive results from these models are evaluated using receiver operating characteristic (ROC) curves and their area under the curve (AUC) values. While the developed models did not show improvements in predictive accuracy, they did show that change point analysis and measures of entropy and long-term memory can be useful tools in predicting postsurgical adverse outcomes.