ArticleCommunications medicine2022
Personalized antibiograms for machine learning driven antibiotic selection.
Article in Communications medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers, 4 of them syntheses that pooled it.
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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
46 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- AI-based clinical decision support systems for antibiotic therapy in common adult infections: a systematic review from development to clinical implementation.Frontiers in digital health · 2026Pooled it
- Artificial intelligence in antimicrobial stewardship: a systematic review and meta-analysis of predictive performance and diagnostic accuracy.European journal of clinical microbiology & infectious diseases : official publication of the European Society of Clinical Microbiology · 2025Pooled it
- Pooled it
- Pooled it
- Machine-learning prediction of urine-culture positivity in a multicentre test-ordered cohort: Model development and internal validation.BJUI compass · 2026Article
- Point-Counterpoint: The "obsoletogram"-are annual hospital antibiograms still relevant?Journal of clinical microbiology · 2026Article
- XGBoost (eXtreme Gradient Boosting) Can Predict Organisms Growing in Urine Culture from the Emergency Department.The western journal of emergency medicine · 2026Article
- Towards functional precision medicine? Evidence standards of organoids as patient-specific models.History and philosophy of the life sciences · 2026Article
- Machine Learning Model for Predicting Multidrug Resistance in ClinicalDiagnostics (Basel, Switzerland) · 2026Article
- Algorithmic antibiotic decision-making in urinary tract infection using prescriber-informed prediction of treatment utility.NPJ digital medicine · 2026Article
- Artificial intelligence for improving decision-making in bacterial infection management: a narrative review.The Journal of antimicrobial chemotherapy · 2026Review
- The institutional antibiogram: Time for a makeover?Journal of hospital medicine · 2026Article
- Optimal antibiotic use in the intensive care unit.Critical care (London, England) · 2025Review
- Machine Learning Model for Predicting Multidrug Resistance in ClinicalAntibiotics (Basel, Switzerland) · 2025Article
- The relationship between respiratory tract infections caused by toxin-producing bacteria in burn patients during COVID-19: pathogenesis, diagnostics and novel therapies.Journal of medical microbiology · 2025Review
- Monitoring strategies for continuous evaluation of deployed clinical prediction models.Journal of biomedical informatics · 2025Article
- Antibiotic Resistance Microbiology Dataset (ARMD): A Resource for Antimicrobial Resistance from EHRs.Scientific data · 2025Article
- Antibiotic Resistance Microbiology Dataset (ARMD): A Resource for Antimicrobial Resistance from EHRs.ArXiv · 2025Article
- Artificial intelligence in Combating Antimicrobial Resistance.Archives of Razi Institute · 2025Review
- Antibiotic De-Escalation in the Intensive Care Unit: Rationale and Potential Strategies.Antibiotics (Basel, Switzerland) · 2025Review
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Background: The Centers for Disease Control and Prevention identify antibiotic prescribing stewardship as the most important action to combat increasing antibiotic resistance. Clinicians balance broad empiric antibiotic coverage vs. precision coverage targeting only the most likely pathogens. We investigate the utility of machine learning-based clinical decision support for antibiotic prescribing stewardship. Methods: In this retrospective multi-site study, we developed machine learning models that predict antibiotic susceptibility patterns (personalized antibiograms) using electronic health record data of 8342 infections from Stanford emergency departments and 15,806 uncomplicated urinary tract infections from Massachusetts General Hospital and Brigham & Women's Hospital in Boston. We assessed the trade-off between broad-spectrum and precise antibiotic prescribing using linear programming. Results: We find in Stanford data that personalized antibiograms reallocate clinician antibiotic selections with a coverage rate (fraction of infections covered by treatment) of 85.9%; similar to clinician performance (84.3% Conclusions: Precision empiric antibiotic prescribing with personalized antibiograms could improve patient safety and antibiotic stewardship by reducing unnecessary use of broad-spectrum antibiotics that breed a growing tide of resistant organisms.
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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.