Machine learning for predicting sepsis in-hospital mortality: an important start

Abstract

We read with interest the article by R. Andrew Taylor et al.¹ This article highlights the potential of random forest to correctly classify sepsis in-hospital mortality. The authors demonstrate superior predictive performance of random forest over methods traditionally used in emergency medicine, classification and regression tree (CART) and a generalized linear mixed model (GLMM), by comparing the area under the ROC curves. Random forest is an improvement over CART because it averages over many (bootstrap aggregated) trees, so it was not a surprise that it outperformed CART, which fits only one tree.

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