TransAMR: An Interpretable Transformer Model for Accurate Prediction of Antimicrobial Resistance Using Antibiotic Administration Data

Tharmakulasingam, Mukunthan; Wang, Wenwu; Kerby, Michael; Ragione, Roberto M. La; Fernando, Anil

doi:10.1109/access.2023.3296221

Cited by 2 publications

(2 citation statements)

References 22 publications

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“…This constitutes a potential channel for the exchange of antimicrobial resistance (AMR) with humans. The articles by Hur et al (2022) and by Tharmakulasingam et al (2023) explored the automatic extraction of antibiotic data in the veterinary field. It is worth noting that these studies focused deeply on automatic extraction, explaining in detail the tools created specifically for this purpose.…”

Section: Automatic Extraction In Veterinary Medicinementioning

confidence: 99%

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Automated extraction of standardized antibiotic resistance and prescription data from laboratory information systems and electronic health records: a narrative review

Cappello,

Murgia,

Giacobbe

et al. 2024

Front. Antibiot.

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Antimicrobial resistance in bacteria has been associated with significant morbidity and mortality in hospitalized patients. In the era of big data and of the consequent frequent need for large study populations, manual collection of data for research studies on antimicrobial resistance and antibiotic use has become extremely time-consuming and sometimes impossible to be accomplished by overwhelmed healthcare personnel. In this review, we discuss relevant concepts pertaining to the automated extraction of antibiotic resistance and antibiotic prescription data from laboratory information systems and electronic health records to be used in clinical studies, starting from the currently available literature on the topic. Leveraging automatic extraction and standardization of antimicrobial resistance and antibiotic prescription data is an tremendous opportunity to improve the care of future patients with severe infections caused by multidrug-resistant organisms, and should not be missed.

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Section: Automatic Extraction In Veterinary Medicinementioning

confidence: 99%

“…The article by Tharmakulasingam et al (2023) illustrates a novel way to predict development of AMR in bacteria. They used a 1D-Transformer, which helped to understand information about antibiotic use.…”

Section: Automatic Extraction In Veterinary Medicinementioning

confidence: 99%