Personalising intravenous to oral antibiotic switch decision making through fair interpretable machine learning

William J. Bolton (), Richard Wilson, Mark Gilchrist, Pantelis Georgiou, Alison Holmes and Timothy M. Rawson
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William J. Bolton: Imperial College London
Richard Wilson: Imperial College London
Mark Gilchrist: Imperial College London
Pantelis Georgiou: Imperial College London
Alison Holmes: Imperial College London
Timothy M. Rawson: Imperial College London

Nature Communications, 2024, vol. 15, issue 1, 1-13

Abstract: Abstract Antimicrobial resistance (AMR) and healthcare associated infections pose a significant threat globally. One key prevention strategy is to follow antimicrobial stewardship practices, in particular, to maximise targeted oral therapy and reduce the use of indwelling vascular devices for intravenous (IV) administration. Appreciating when an individual patient can switch from IV to oral antibiotic treatment is often non-trivial and not standardised. To tackle this problem we created a machine learning model to predict when a patient could switch based on routinely collected clinical parameters. 10,362 unique intensive care unit stays were extracted and two informative feature sets identified. Our best model achieved a mean AUROC of 0.80 (SD 0.01) on the hold-out set while not being biased to individuals protected characteristics. Interpretability methodologies were employed to create clinically useful visual explanations. In summary, our model provides individualised, fair, and interpretable predictions for when a patient could switch from IV-to-oral antibiotic treatment. Prospectively evaluation of safety and efficacy is needed before such technology can be applied clinically.

Date: 2024
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DOI: 10.1038/s41467-024-44740-2

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