ArticleScientific reports2022
Machine learning to predict the development of recurrent urinary tract infection related to single uropathogen, Escherichia coli.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed.
- Explainable machine learning for early detection ofFrontiers in cellular and infection microbiology · 2026Article
- Prediction of symptomatic and asymptomatic bacteriuria in spinal cord injury patients using machine learning.Microbiome · 2025Article
- Comparative analysis of machine learning models for predicting hospital- and community-associated urinary tract infections using demographic, hospital, and socioeconomic predictors.The Journal of hospital infection · 2025Article
- Use of Artificial Intelligence Methods for Improved Diagnosis of Urinary Tract Infections and Urinary Stone Disease.Journal of clinical medicine · 2025Review
- From Preliminary Urinalysis to Decision Support: Machine Learning for UTI Prediction in Real-World Laboratory Data.Journal of personalized medicine · 2025Article
- Urinary Bladder Acute Inflammations and Nephritis of the Renal Pelvis: Diagnosis Using Fine-Tuned Large Language Models.Journal of personalized medicine · 2025Article
- Clinical characteristics and nomogram model for predicting the risk of recurrence of complicated urinary tract infection in pediatric patients.Scientific reports · 2024Article
- Random forest differentiation of Escherichia coli in elderly sepsis using biomarkers and infectious sites.Scientific reports · 2024Article
- The Critical Role of Intracellular Bacterial Communities in Uncomplicated Recurrent Urinary Cystitis: A Comprehensive Review of Detection Methods and Diagnostic Potential.International neurourology journal · 2024Article
- Detection of Patients at Risk of Multidrug-Resistant Enterobacteriaceae Infection Using Graph Neural Networks: A Retrospective Study.Health data science · 2023Article
- Review of non-antibiotic treatment and prevention of recurrent UTIs - a summary of current guidance and suggested treatment algorithm.Therapeutic advances in infectious diseaseArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recurrent 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. Three machine learning models, logistic regression (LR), decision tree (DT), and random forest (RF) were built for the RUTI prediction. The RF model had higher prediction accuracy than LR and DT (0.700, 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 classification accuracy (up to 0.92 in stage 1 and 0.94 in stage 2) in certain subgroup patients in different scenarios. In conclusion, this study provided validated machine learning models and RF could provide a better accuracy 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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.