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Machine Learning for Predicting Sepsis In‐hospital Mortality: An Important Start
Author(s) -
Scott Halden,
Colborn Kathryn
Publication year - 2016
Publication title -
academic emergency medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.221
H-Index - 124
eISSN - 1553-2712
pISSN - 1069-6563
DOI - 10.1111/acem.13009
Subject(s) - random forest , machine learning , artificial intelligence , cart , decision tree , gradient boosting , medicine , data set , test set , statistics , tree (set theory) , regression , computer science , mathematics , mechanical engineering , mathematical analysis , engineering
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. This article is protected by copyright. All rights reserved.