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Machine learning for clinical decision support in infectious diseases: a narrative review of current applications
516
Zitationen
8
Autoren
2019
Jahr
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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