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Machine learning in infection management using routine electronic health records: tools, techniques, and reporting of future technologies.

Authors
  • Luz, C F1
  • Vollmer, M2
  • Decruyenaere, J3
  • Nijsten, M W4
  • Glasner, C5
  • Sinha, B5
  • 1 University of Groningen, University Medical Center Groningen, Department of Medical Microbiology and Infection Prevention, Groningen, the Netherlands. Electronic address: [email protected] , (Netherlands)
  • 2 Institute of Bioinformatics, University Medicine Greifswald, Greifswald, Germany. , (Germany)
  • 3 Ghent University, Ghent University Hospital, Department of Intensive Care, Ghent, Belgium. , (Belgium)
  • 4 University of Groningen, University Medical Center Groningen, Department of Critical Care, Groningen, the Netherlands. , (Netherlands)
  • 5 University of Groningen, University Medical Center Groningen, Department of Medical Microbiology and Infection Prevention, Groningen, the Netherlands. , (Netherlands)
Type
Published Article
Journal
Clinical Microbiology and Infection
Publisher
Elsevier
Publication Date
Oct 01, 2020
Volume
26
Issue
10
Pages
1291–1299
Identifiers
DOI: 10.1016/j.cmi.2020.02.003
PMID: 32061798
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

Machine learning (ML) is increasingly being used in many areas of health care. Its use in infection management is catching up as identified in a recent review in this journal. We present here a complementary review to this work. To support clinicians and researchers in navigating through the methodological aspects of ML approaches in the field of infection management. A Medline search was performed with the keywords artificial intelligence, machine learning, infection∗, and infectious disease∗ for the years 2014-2019. Studies using routinely available electronic hospital record data from an inpatient setting with a focus on bacterial and fungal infections were included. Fifty-two studies were included and divided into six groups based on their focus. These studies covered detection/prediction of sepsis (n = 19), hospital-acquired infections (n = 11), surgical site infections and other postoperative infections (n = 11), microbiological test results (n = 4), infections in general (n = 2), musculoskeletal infections (n = 2), and other topics (urinary tract infections, deep fungal infections, antimicrobial prescriptions; n = 1 each). In total, 35 different ML techniques were used. Logistic regression was applied in 18 studies followed by random forest, support vector machines, and artificial neural networks in 18, 12, and seven studies, respectively. Overall, the studies were very heterogeneous in their approach and their reporting. Detailed information on data handling and software code was often missing. Validation on new datasets and/or in other institutions was rarely done. Clinical studies on the impact of ML in infection management were lacking. Promising approaches for ML use in infectious diseases were identified. But building trust in these new technologies will require improved reporting. Explainability and interpretability of the models used were rarely addressed and should be further explored. Independent model validation and clinical studies evaluating the added value of ML approaches are needed. Copyright © 2020 The Authors. Published by Elsevier Ltd.. All rights reserved.

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