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

2019522 citationsReviewhybrid Open Access

Authors

Nathan Peiffer‐Smadja · Université Paris Cité

Timothy M. Rawson · Imperial College London

Raheelah Ahmad · National Institute for Health Research

Albert Buchard · Babylon Health

Pantelis Georgiou · Imperial College London

François-Xavier Lescure · Assistance Publique – Hôpitaux de Paris

Gabriel Birgand

Abstract

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.

Topics & Keywords

COVID-19 diagnosis using AI Artificial Intelligence in Healthcare and Education Machine Learning in Healthcare

Publication Details

Published in: Clinical Microbiology and Infection

Volume 26, Issue 5, pp. 584-595

DOI: 10.1016/j.cmi.2019.09.009

Field-Weighted Citation Impact: 30.03

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

Authors

Abstract

Topics & Keywords

Publication Details