Economic Consequences of Road Traffic Injuries. Application of the Super Learner algorithm

We perform a prediction analysis using methods of supervised machine learning on a set of outcomes that measure economic consequences of road traffic injuries. We employ several parametric and non-parametric algorithms including regularised regressions, decision trees and random forests to model statistically challenging empirical distributions and identify the key risk groups. In addition to a traditional outcome of interest – health care costs – we predict net monetary benefits from treatment, and productivity losses measured by the probability to return to work after the injury. Using the predictions of each selected algorithm we construct an ensemble machine learning algorithm - the Super Learner algorithm. Our findings demonstrate that the Super Learner is effective and performs best in predicting all outcomes. Further analysis of predictions by different groups of patients play an important role in the understanding of key risk factors for higher costs and poorer outcomes and offers a deeper understanding of risk in the health care sector.