Md Mohaimenul Islam1, Tahmina Nasrin1, Bruno Andreas Walther2, Chieh-Chen Wu1, Hsuan-Chia Yang3, Yu-Chuan Li4. 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.
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.
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.
Authors: Laura Evans; Andrew Rhodes; Waleed Alhazzani; Massimo Antonelli; Craig M Coopersmith; Craig French; Flávia R Machado; Lauralyn Mcintyre; Marlies Ostermann; Hallie C Prescott; Christa Schorr; Steven Simpson; W Joost Wiersinga; Fayez Alshamsi; Derek C Angus; Yaseen Arabi; Luciano Azevedo; Richard Beale; Gregory Beilman; Emilie Belley-Cote; Lisa Burry; Maurizio Cecconi; John Centofanti; Angel Coz Yataco; Jan De Waele; R Phillip Dellinger; Kent Doi; Bin Du; Elisa Estenssoro; Ricard Ferrer; Charles Gomersall; Carol Hodgson; Morten Hylander Møller; Theodore Iwashyna; Shevin Jacob; Ruth Kleinpell; Michael Klompas; Younsuck Koh; Anand Kumar; Arthur Kwizera; Suzana Lobo; Henry Masur; Steven McGloughlin; Sangeeta Mehta; Yatin Mehta; Mervyn Mer; Mark Nunnally; Simon Oczkowski; Tiffany Osborn; Elizabeth Papathanassoglou; Anders Perner; Michael Puskarich; Jason Roberts; William Schweickert; Maureen Seckel; Jonathan Sevransky; Charles L Sprung; Tobias Welte; Janice Zimmerman; Mitchell Levy Journal: Intensive Care Med Date: 2021-10-02 Impact factor: 17.440
Authors: Zina M Ibrahim; Honghan Wu; Ahmed Hamoud; Lukas Stappen; Richard J B Dobson; Andrea Agarossi Journal: J Am Med Inform Assoc Date: 2020-03-01 Impact factor: 4.497
Authors: Md Mohaimenul Islam; Tahmina Nasrin Poly; Bruno Andreas Walther; Hsuan Chia Yang; Yu-Chuan Jack Li Journal: J Clin Med Date: 2020-04-03 Impact factor: 4.241