Predictive analytics have been widely used in the literature with respect to laparoscopic surgery and risk stratification. However, most predictive analytics in this field exploit generalized linear models for predictive purposes, which are limited by model assumptions—including linearity between response variables and additive interactions between variables. In many instances, such assumptions may not hold true, and the complex relationship between predictors and response variables is usually unknown. To address this limitation, machine-learning algorithms can be employed to model the underlying data. The advantage of machine learning algorithms is that they usually do not require strict assumptions regarding data structure, and they are able to learn complex functional forms using a nonparametric approach. Furthermore, two or more machine learning algorithms can be synthesized to further improve predictive accuracy. Such a process is referred to as ensemble modeling, and it has been used broadly in various industries. However, this approach has not been widely reported in the laparoscopic surgical literature due to its complexity in both model training and interpretation. With this technical note, we provide a comprehensive overview of the ensemble-modeling technique and a step-by-step tutorial on how to implement ensemble modeling.
