logo
ResearchBunny Logo
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

Related Publications

Explore these studies to deepen your understanding of the subject.

Machine learning based suicide prediction and development of suicide vulnerability index for US counties
Medicine and Health

Machine learning based suicide prediction and development of suicide vulnerability index for US counties

V. Kumar, K. K. Sznajder, et al.

Machine learning-based prediction of in-hospital death for patients with takotsubo syndrome: The InterTAK-ML model
Medicine and Health

Machine learning-based prediction of in-hospital death for patients with takotsubo syndrome: The InterTAK-ML model

O. D. Filippo, V. L. Cammann, et al.

Prediction of mortality risk and duration of hospitalization of COVID-19 patients with chronic comorbidities based on machine learning algorithms
Medicine and Health

Prediction of mortality risk and duration of hospitalization of COVID-19 patients with chronic comorbidities based on machine learning algorithms

P. Amiri, M. Montazeri, et al.

Machine learning-based prediction of COVID-19 diagnosis based on symptoms
Medicine and Health

Machine learning-based prediction of COVID-19 diagnosis based on symptoms

Y. Zoabi, S. Deri-rozov, et al.

Listen, Learn & Level Up
Over 10,000 hours of research content in 25+ fields, available in 12+ languages.
No more digging through PDFs—just hit play and absorb the world's latest research in your language, on your time.
listen to research audio papers with researchbunny