Prediction of sepsis patients using machine learning approach: A meta-analysis

Md Mohaimenul Islam; Tahmina Nasrin; Bruno Andreas Walther; Chieh-Chen Wu; Hsuan-Chia Yang; Yu-Chuan Li

doi:10.1016/j.cmpb.2018.12.027

Prediction of sepsis patients using machine learning approach: A meta-analysis

Comput Methods Programs Biomed. 2019 Mar:170:1-9. doi: 10.1016/j.cmpb.2018.12.027. Epub 2018 Dec 26.

Authors

Md Mohaimenul Islam¹, Tahmina Nasrin¹, Bruno Andreas Walther², Chieh-Chen Wu¹, Hsuan-Chia Yang³, Yu-Chuan Li⁴

Affiliations

¹ Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan; International Center for Health Information Technology, Taipei Medical University, Taipei, Taiwan.
² Department of Biological Sciences, National Sun Yat-sen University, Gushan District, Kaohsiung City, 804, Taiwan.
³ International Center for Health Information Technology, Taipei Medical University, Taipei, Taiwan.
⁴ Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan; International Center for Health Information Technology, Taipei Medical University, Taipei, Taiwan; Department of Dermatology, Wan Fang Hospital, Taipei, Taiwan; TMU Research Center of Cancer Translational Medicine, Taipei, Taiwan.

PMID: 30712598
DOI: 10.1016/j.cmpb.2018.12.027

Abstract

Study objective: Sepsis is a common and major health crisis in hospitals globally. An innovative and feasible tool for predicting sepsis remains elusive. However, early and accurate prediction of sepsis could help physicians with proper treatments and minimize the diagnostic uncertainty. Machine learning models could help to identify potential clinical variables and provide higher performance than existing traditional low-performance models. We therefore performed a meta-analysis of observational studies to quantify the performance of a machine learning model to predict sepsis.

Methods: A comprehensive literature search was conducted through the electronic database (e.g. PubMed, Scopus, Google Scholar, EMBASE, etc.) between January 1, 2000, and March 1, 2018. All the studies published in English and reporting the sepsis prediction using machine learning algorithms were considered in this study. Two authors independently extracted valuable information from the included studies. Inclusion and exclusion of studies were based on the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines.

Results: A total of 7 out of 135 studies met all of our inclusion criteria. For machine learning models, the pooled area under receiving operating curve (SAUROC) for predicting sepsis onset 3 to 4 h before, was 0.89 (95%CI: 0.86-0.92); sensitivity 0.81 (95%CI:0.80-0.81), and specificity 0.72 (95%CI:0.72-0.72) whereas the pooled SAUROC for SIRS, MEWS, and SOFA was 0.70, 0.50, and 0.78. Additionally, diagnostic odd ratio for machine learning, SIRS, MEWS, and SOFA was 15.17 (95%CI: 9.51-24.20), 3.23 (95%CI: 1.52-6.87), 31.99 (95% CI: 1.54-666.74), and 3.75(95%CI: 2.06-6.83).

Conclusion: Our study findings suggest that the machine learning approach had a better performance than the existing sepsis scoring systems in predicting sepsis.

Keywords: Area under receiver operating curve; Diagnostic odd ratio; Machine learning; Sepsis.

Publication types

Meta-Analysis

MeSH terms

Algorithms
Area Under Curve
Forecasting
Humans
Machine Learning*
Sepsis / diagnosis*
Sepsis / drug therapy