aceAdvances in machine learning and deep learning for antimicrobial resistance prediction
Antimicrobial resistance (AMR) is a major public health risk. Testing antimicrobial susceptibility in patients using conventional methods takes a week or more to deliver results, advances in artificial intelligence (AI), including machine learning (ML), and deep learning (DL), have created an opportunity to predict AMR with speed by combining data from different sources such as genomic, transcriptomic, proteomic, phenotypic, and clinical information. Recent advances in whole genome sequencing, metagenomic method…
Machine learning and deep learning can combine genomic, proteomic, phenotypic and clinical data to predict antimicrobial resistance faster than conventional susceptibility testing that takes a week or more.
The review states that building predictive models is hindered by lack of dataset diversity, class imbalance, inconsistent testing methods and limited interpretability, and that further validation is needed.
Evidence
- Peer-reviewedDiscover Applied Sciences2026-10-20
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Truvace Impact Record TRV-2026-2102, v1: “Advances in machine learning and deep learning for antimicrobial resistance prediction.” Truvace, 2026-10-21. /record/TRV-2026-2102 (accessed at citation time). sha256 69aac09cfae75567…
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