Predicting Sepsis Mortality in a Population-Based National Database: Machine Learning Approach

Autor: James Yeongjun Park, Tzu-Chun Hsu, Jiun-Ruey Hu, Chun-Yuan Chen, Wan-Ting Hsu, Matthew Lee, Joshua Ho, Chien-Chang Lee
Jazyk: angličtina
Rok vydání: 2022
Předmět:
Zdroj: Journal of Medical Internet Research, Vol 24, Iss 4, p e29982 (2022)
Druh dokumentu: article
ISSN: 1438-8871
DOI: 10.2196/29982
Popis: BackgroundAlthough machine learning (ML) algorithms have been applied to point-of-care sepsis prognostication, ML has not been used to predict sepsis mortality in an administrative database. Therefore, we examined the performance of common ML algorithms in predicting sepsis mortality in adult patients with sepsis and compared it with that of the conventional context knowledge–based logistic regression approach. ObjectiveThe aim of this study is to examine the performance of common ML algorithms in predicting sepsis mortality in adult patients with sepsis and compare it with that of the conventional context knowledge–based logistic regression approach. MethodsWe examined inpatient admissions for sepsis in the US National Inpatient Sample using hospitalizations in 2010-2013 as the training data set. We developed four ML models to predict in-hospital mortality: logistic regression with least absolute shrinkage and selection operator regularization, random forest, gradient-boosted decision tree, and deep neural network. To estimate their performance, we compared our models with the Super Learner model. Using hospitalizations in 2014 as the testing data set, we examined the models’ area under the receiver operating characteristic curve (AUC), confusion matrix results, and net reclassification improvement. ResultsHospitalizations of 923,759 adults were included in the analysis. Compared with the reference logistic regression (AUC: 0.786, 95% CI 0.783-0.788), all ML models showed superior discriminative ability (P
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