Articlenpj antimicrobials and resistance2023
Interpretable machine learning-based decision support for prediction of antibiotic resistance for complicated urinary tract infections.
Article in npj antimicrobials and resistance, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
What it found
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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.
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Who cites it
17 citing papers in PubMed.
- Early Prediction of Carbapenem Resistance in Gram-Negative Organisms at Culture Ordering from Routinely Collected Hospital Data: Temporal and External Validation of ORDER-CARB.Infectious diseases and therapy · 2026Article
- Comparison of OneChoice AI-Based Clinical Decision Support Recommendations with Infectious Disease Specialists and Non-Specialists for Empirical Urinary Tract Infection Therapy in Lima, Peru.Diagnostics (Basel, Switzerland) · 2026Article
- Artificial intelligence research in journals indexed in the web of science "infectious diseases" category: a bibliometric analysis, 2016-2025.Infection · 2026Article
- A deep learning approach for predicting antimicrobial resistance in urinary tract infections.BMC urology · 2026Article
- Point-Counterpoint: The "obsoletogram"-are annual hospital antibiograms still relevant?Journal of clinical microbiology · 2026Article
- Next-Generation Target Discovery in ESKAPE Pathogens: An AI-Driven Framework from Omics-Based to Systems-Level Modeling and Clinical Translation.Antibiotics (Basel, Switzerland) · 2026Review
- XGBoost (eXtreme Gradient Boosting) Can Predict Organisms Growing in Urine Culture from the Emergency Department.The western journal of emergency medicine · 2026Article
- Artificial intelligence in optimizing antimicrobial therapy for gastro-renal disorders.Frontiers in cellular and infection microbiology · 2026Review
- Duration of same-species antimicrobial non-susceptibility persistence in urinary gram-negative bacilli: a 12-year within-patient cohort study.Frontiers in cellular and infection microbiology · 2026Article
- Deep Learning-Based Rapid Identification ofRisk management and healthcare policy · 2026Article
- Machine Learning Model for Predicting Multidrug Resistance in ClinicalAntibiotics (Basel, Switzerland) · 2025Article
- Status and implications of the knowledge, attitudes and practices towards AWaRe antibiotic use, resistance and stewardship among low- and middle-income countries.JAC-antimicrobial resistance · 2025Review
- Leveraging innovative diagnostics as a tool to contain superbugs.Antonie van Leeuwenhoek · 2025Review
- Machine learning to predict urine culture antibiotic sensitivities in the emergency department.Heliyon · 2025Article
- Machine learning and clinician predictions of antibiotic resistance in Enterobacterales bloodstream infections.The Journal of infection · 2025Article
- FromFrontiers in medicine · 2025Article
- Decoding 2.3 million ECGs: interpretable deep learning for advancing cardiovascular diagnosis and mortality risk stratification.European heart journal. Digital health · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Urinary tract infections are one of the most common bacterial infections worldwide; however, increasing antimicrobial resistance in bacterial pathogens is making it challenging for clinicians to correctly prescribe patients appropriate antibiotics. In this study, we present four interpretable machine learning-based decision support algorithms for predicting antimicrobial resistance. Using electronic health record data from a large cohort of patients diagnosed with potentially complicated UTIs, we demonstrate high predictability of antibiotic resistance across four antibiotics - nitrofurantoin, co-trimoxazole, ciprofloxacin, and levofloxacin. We additionally demonstrate the generalizability of our methods on a separate cohort of patients with uncomplicated UTIs, demonstrating that machine learning-driven approaches can help alleviate the potential of administering non-susceptible treatments, facilitate rapid effective clinical interventions, and enable personalized treatment suggestions. Additionally, these techniques present the benefit of providing model interpretability, explaining the basis for generated predictions.
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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.