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@article{Peiffer-Smadja:2020:10.1016/j.cmi.2019.09.009,
author = {Peiffer-Smadja, N and Rawson, TM and Ahmad, R and Buchard, A and Pantelis, G and Lescure, F-X and Birgand, G and Holmes, A},
doi = {10.1016/j.cmi.2019.09.009},
journal = {Clinical Microbiology and Infection},
pages = {584--595},
title = {Machine learning for clinical decision support in infectious diseases: A narrative review of current applications},
url = {http://dx.doi.org/10.1016/j.cmi.2019.09.009},
volume = {26},
year = {2020}
}

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TY  - JOUR
AB  - BACKGROUNDMachine 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). OBJECTIVESWe aim to inform clinicians about the use of ML for diagnosis, classification, outcome prediction and antimicrobial management in ID.SOURCESReferences 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.CONTENTWe found 60 unique 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%). IMPLICATIONSConsidering comprehensive patient data from socioeconomically diverse health care 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 cli
AU  - Peiffer-Smadja,N
AU  - Rawson,TM
AU  - Ahmad,R
AU  - Buchard,A
AU  - Pantelis,G
AU  - Lescure,F-X
AU  - Birgand,G
AU  - Holmes,A
DO  - 10.1016/j.cmi.2019.09.009
EP  - 595
PY  - 2020///
SN  - 1198-743X
SP  - 584
TI  - Machine learning for clinical decision support in infectious diseases: A narrative review of current applications
T2  - Clinical Microbiology and Infection
UR  - http://dx.doi.org/10.1016/j.cmi.2019.09.009
UR  - https://www.sciencedirect.com/science/article/pii/S1198743X1930494X?via%3Dihub
UR  - http://hdl.handle.net/10044/1/73361
VL  - 26
ER  -

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