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Interpretable machine learning-based decision support for prediction of antibiotic resistance for complicated urinary tract infections

Medicine and Health

Interpretable machine learning-based decision support for prediction of antibiotic resistance for complicated urinary tract infections

J. Yang, D. W. Eyre, et al.

Discover how a collaborative team of researchers, including Jenny Yang and David W. Eyre, has developed innovative machine learning algorithms that predict antibiotic resistance in urinary tract infections. Their work not only enhances treatment efficacy but also promotes personalized care through interpretability in model design.

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Playback language: English
Abstract
This study presents four interpretable machine learning algorithms for predicting antimicrobial resistance in complicated urinary tract infections (UTIs). Using electronic health record data, the algorithms demonstrate high predictability of antibiotic resistance across four antibiotics. The methods' generalizability is shown on a separate uncomplicated UTI cohort. The approach aims to reduce ineffective treatments, facilitate rapid interventions, and enable personalized treatment suggestions, while also providing model interpretability.
Publisher
npj Antimicrobials & Resistance
Published On
Nov 02, 2023
Authors
Jenny Yang, David W. Eyre, Lei Lu, David A. Clifton
Tags
antimicrobial resistance
machine learning
urinary tract infections
predictive algorithms
personalized treatment

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