Machine learning in predicting antimicrobial resistance: a systematic review and meta-analysis
Antimicrobial resistance (AMR) is a global health threat, rapid and timely identification of AMR improves patient prognosis and reduces inappropriate antibiotic use. We systematically searched relevant literature in PubMed, Web of Science, Embase and Institute of Electrical and Electronics Engineers prior to Sep 28, 2021. The study that deployed machine learning or risk score as tool to predict AMR was included in the final review.
ML might be a potential technology for AMR prediction. However, retrospective methodology for model development, nonstandard data processing and scarcity of validation in a randomized controlled trial or real-world study limit the application of these models in clinical practice.
OUR UNDERWRITERS
Unrestricted financial support by:
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Antimicrobial Resistance Fighter Coalition |
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Bangalore Bioinnovation Centre |
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Evotec |
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JSS University |
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INTERNATIONAL FEDERATION PHARMACEUTICAL MANUFACTURERS & ASSOCIATIONS |
Global Ambassador Network
Welcome at the AMR Insights Ambassador Network!
The AMR Insights Ambassador Network is a growing, distinctive group of professionals who stand out for their commitment, willingness to cooperate and open attitude to combat Antimicrobial resistance (AMR).
