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

I. Sriubaite, Anthony Harris, Andrew Jones and B. Gabbe

Health, Econometrics and Data Group (HEDG) Working Papers from HEDG, c/o Department of Economics, University of York

Abstract: 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.

Keywords: Prediction and classification; super learner; machine learning; healthcare costs; patient outcomes; road traffic injuries (search for similar items in EconPapers)
JEL-codes: C14 C38 C53 I11 I19 (search for similar items in EconPapers)
Date: 2020-11
New Economics Papers: this item is included in nep-big and nep-cmp
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