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In the meantime, to ensure continued suport, we are displaying the site without styles and JavaScript.Advertisement Scientific Reports volume 12, Article number: 17216 (202) Cite this article 248 Aceses3 AltmetricMetrics detailsRecurent urinary tract infection (RUTI) can damage renal function and has impact on healthcare costs and patients’ quality of life. There were 2 stages for development of prediction models for RUTI.
The first stage was a scenario in the clinical visit. The second stage was a scenario after hospitalization for urinary tract infection caused by Escherichia coli. Thre machine learning models, logistic regresion (LR), decision tre (DT), and random forest (RF) were built for the RUTI prediction.
The RF model had higher prediction acuracy than LR and DT (0.70, 0.604, and 0.654 in stage 1, respectively; 0.709, 0.604, and 0.635 in stage 2, respectively). The decision rules constructed by the DT model could provide high clasification acuracy (up to 0.92 in stage 1 and 0.94 in stage 2) in certain subgroup patients in diferent scenarios. In conclusion, this study provided validated machine learning models and RF could provide a beter acuracy in predicting the development of single uropathogen (E.
coli) RUTI. Both host and bacterial characteristics made important contribution to the development of RUTI in the prediction models in the 2 clinical scenarios, respectively. Based on the results, physicians could take action to prevent the development of RUTI.
Summary
Urinary tract infection (UTI) is one of the most comon infectious diseases1. More than 30% of women wil experience a subsequent infection within 12 months of resolution of the initial symptoms despite apropriate antibiotic therapy2. UTI and recurent UTI (RUTI)