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Updated: Jun 9, 2025
Establishment and Characterization of UTI and CAUTI in a Mouse Model
Published on: June 23, 2015
Estimating individual risk of catheter-associated urinary tract infections using explainable artificial intelligence
Herdiantri Sufriyana1, Chieh Chen2, Hua-Sheng Chiu3
1Institute of Biomedical Informatics, College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan; Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan.
Background:
Catheter-associated urinary tract infections (CAUTIs) increase clinical burdens. Identifying the high-risk patients is crucial. We aimed to develop and externally validate an explainable, prognostic prediction model of CAUTIs among hospitalized individuals receiving urinary catheterization.
Methods:
A retrospective cohort paradigm was applied for model development and validation using data from 2 hospitals and used the third hospital's data for external validation. Machine learning algorithms were applied for predictive modeling. We evaluated the calibration, clinical utility, and discrimination ability to choose the best model by the validation set. The best model was assessed for the explainability.
Results:
We included 122,417 instances from 20-to-75-year-old subjects. Fourteen predictors were selected from 20 candidates. The best model was the random forest for prediction within 6days. It detected 97.63% (95% confidence interval [CI]: ± 0.06%) CAUTI positive, and 97.36% (95% CI: ± 0.07%) of individuals that were predicted to be CAUTI negative were true negatives. Among those predicted to be CAUTI positives, we expected 22.85% (95% CI: ± 0.07%) of them to truly be high-risk individuals. We provide a web-based application and a paper-based nomogram for using this model.
Conclusions:
Our prediction model accurately detected most CAUTI-positive cases, while most predicted negative individuals were correctly ruled out.
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