Machine learning for the prediction of sepsis: a systematic review and meta-analysis of diagnostic test accuracy

• Published in: Intensive care medicine Vol. 46; no. 3; pp. 383 - 400
• Main Authors: Fleuren, Lucas M., Klausch, Thomas L. T., Zwager, Charlotte L., Schoonmade, Linda J., Guo, Tingjie, Roggeveen, Luca F., Swart, Eleonora L., Girbes, Armand R. J., Thoral, Patrick, Ercole, Ari, Hoogendoorn, Mark, Elbers, Paul W. G.
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.03.2020; Springer; Springer Nature B.V
• Subjects: Accuracy; Analysis; Anesthesiology; Critical Care Medicine; Diagnostic systems; Diagnostic tests; Diagnostic Tests, Routine; Emergency medical care; Emergency medical services; Emergency Medicine; Humans; Infection; Intensive; Intensive care; Learning algorithms; Machine Learning; Medical diagnosis; Medical tests; Medicine; Medicine & Public Health; Meta-analysis; Multivariate analysis; Pain Medicine; Pediatrics; Pneumology/Respiratory System; Predictions; Quality assessment; Quality control; Real time; Retrospective Studies; Reviews; Sepsis; Sepsis - diagnosis; Septic shock; Shock, Septic; Systematic Review; Netherlands; Sepsis; Septic shock; Systematic review; Machine learning; Prediction; Meta-analysis

Purpose Early clinical recognition of sepsis can be challenging. With the advancement of machine learning, promising real-time models to predict sepsis have emerged. We assessed their performance by carrying out a systematic review and meta-analysis. Methods A systematic search was performed in PubM...