Format

Send to

Choose Destination
Clin Microbiol Infect. 2019 Sep 17. pii: S1198-743X(19)30494-X. doi: 10.1016/j.cmi.2019.09.009. [Epub ahead of print]

Machine learning for clinical decision support in infectious diseases: a narrative review of current applications.

Author information

1
National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, Imperial College London, London, UK; French Institute for Medical Research (Inserm), Infection Antimicrobials Modelling Evolution (IAME), UMR 1137, University Paris Diderot, Paris, France. Electronic address: n.peiffer-smadja@ic.ac.uk.
2
National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, Imperial College London, London, UK.
3
Babylon Health, London, UK.
4
Department of Electrical and Electronic Engineering, Imperial College, London, UK.
5
French Institute for Medical Research (Inserm), Infection Antimicrobials Modelling Evolution (IAME), UMR 1137, University Paris Diderot, Paris, France; Infectious Diseases Department, Bichat-Claude Bernard Hospital, Assistance-Publique Hôpitaux de Paris, Paris, France.

Abstract

BACKGROUND:

Machine learning (ML) is a growing field in medicine. This narrative review describes the current body of literature on ML for clinical decision support in infectious diseases (ID).

OBJECTIVES:

We aim to inform clinicians about the use of ML for diagnosis, classification, outcome prediction and antimicrobial management in ID.

SOURCES:

References for this review were identified through searches of MEDLINE/PubMed, EMBASE, Google Scholar, biorXiv, ACM Digital Library, arXiV and IEEE Xplore Digital Library up to July 2019.

CONTENT:

We found 60 unique ML-clinical decision support systems (ML-CDSS) aiming to assist ID clinicians. Overall, 37 (62%) focused on bacterial infections, 10 (17%) on viral infections, nine (15%) on tuberculosis and four (7%) on any kind of infection. Among them, 20 (33%) addressed the diagnosis of infection, 18 (30%) the prediction, early detection or stratification of sepsis, 13 (22%) the prediction of treatment response, four (7%) the prediction of antibiotic resistance, three (5%) the choice of antibiotic regimen and two (3%) the choice of a combination antiretroviral therapy. The ML-CDSS were developed for intensive care units (n = 24, 40%), ID consultation (n = 15, 25%), medical or surgical wards (n = 13, 20%), emergency department (n = 4, 7%), primary care (n = 3, 5%) and antimicrobial stewardship (n = 1, 2%). Fifty-three ML-CDSS (88%) were developed using data from high-income countries and seven (12%) with data from low- and middle-income countries (LMIC). The evaluation of ML-CDSS was limited to measures of performance (e.g. sensitivity, specificity) for 57 ML-CDSS (95%) and included data in clinical practice for three (5%).

IMPLICATIONS:

Considering comprehensive patient data from socioeconomically diverse healthcare settings, including primary care and LMICs, may improve the ability of ML-CDSS to suggest decisions adapted to various clinical contexts. Currents gaps identified in the evaluation of ML-CDSS must also be addressed in order to know the potential impact of such tools for clinicians and patients.

KEYWORDS:

Artificial intelligence; Clinical decision support system; Infectious diseases; Information technology; Machine learning

PMID:
31539636
DOI:
10.1016/j.cmi.2019.09.009

Supplemental Content

Full text links

Icon for Elsevier Science
Loading ...
Support Center