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September 17, 2019Clinical Microbiology and Infection571 citationsOpen Access

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

NPNathan Peiffer‐SmadjaTRTimothy M. RawsonRARaheelah Ahmad

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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.

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Peiffer‐Smadja et al. (2019) studied this question.

synapsesocial.com/papers/69d76df6f44a16d01ef30f83https://doi.org/10.1016/j.cmi.2019.09.009
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