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

C F Luz; M Vollmer; J Decruyenaere; M W Nijsten; C Glasner; B Sinha

doi:10.1016/j.cmi.2020.02.003

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

Clin Microbiol Infect. 2020 Oct;26(10):1291-1299. doi: 10.1016/j.cmi.2020.02.003. Epub 2020 Feb 13.

Authors

C F Luz¹, M Vollmer², J Decruyenaere³, M W Nijsten⁴, C Glasner⁵, B Sinha⁵

Affiliations

¹ University of Groningen, University Medical Center Groningen, Department of Medical Microbiology and Infection Prevention, Groningen, the Netherlands. Electronic address: c.f.luz@umcg.nl.
² Institute of Bioinformatics, University Medicine Greifswald, Greifswald, Germany.
³ Ghent University, Ghent University Hospital, Department of Intensive Care, Ghent, Belgium.
⁴ University of Groningen, University Medical Center Groningen, Department of Critical Care, Groningen, the Netherlands.
⁵ University of Groningen, University Medical Center Groningen, Department of Medical Microbiology and Infection Prevention, Groningen, the Netherlands.

PMID: 32061798
DOI: 10.1016/j.cmi.2020.02.003

Abstract

Background: 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.

Objectives: To support clinicians and researchers in navigating through the methodological aspects of ML approaches in the field of infection management.

Sources: 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.

Content: 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.

Implications: 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.

Keywords: Algorithms; Artificial intelligence; Electronic health records; Infection; Inpatient; Machine learning; Methods; Review.

Publication types

Review

MeSH terms

Algorithms
Cross Infection / diagnosis
Cross Infection / therapy
Decision Support Systems, Clinical*
Electronic Health Records*
Humans
Machine Learning*
Prognosis
Sepsis / diagnosis*
Sepsis / therapy*
Surgical Wound Infection / diagnosis
Surgical Wound Infection / therapy
Urinary Tract Infections / diagnosis
Urinary Tract Infections / therapy