Articlenpj antimicrobials and resistance2025
Integrating socioeconomic deprivation indices and electronic health record data to predict antimicrobial resistance.
Article in npj antimicrobials and resistance, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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
6 citing papers in PubMed.
- Reimagining antimicrobial resistance: AI-driven predictive epidemiology and the C-AMRE framework for next-generation antibiotic discovery.The Journal of antibiotics · 2026Review
- A One Health Perspective onMicroorganisms · 2026Review
- From surveillance to intelligence: a scoping review of machine learning for antimicrobial resistance surveillance intelligence across One Health.Frontiers in public health · 2026Article
- Exploring opportunities to improve health equity with machine learning and artificial intelligence in healthcare epidemiology.Antimicrobial stewardship & healthcare epidemiology : ASHE · 2026Review
- Cultivating global antimicrobial stewardship: prescribing quality and implementability insights from Portugal's First Hospital National Antimicrobial Prescribing Survey.Antimicrobial resistance and infection control · 2025Article
- Area-based Deprivation Indices and Healthcare-Associated Infections: A Narrative Review of Evidence.Current infectious disease reports · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
Abstract
We developed machine learning models to predict the presence of AMR organisms in blood cultures obtained at the first patient encounter, offering a new and inspiring direction for antimicrobial resistance management. Three supervised machine learning classifiers were used: penalized logistic regression, random forest, and XGBoost, which were used to classify five AMR organisms: ESBL, CRE, AmpC, MRSA, and VRE. The random forest and XGBoost models performed best, with AUC-ROC values of 0.70 and 92.9% negative predictive value, respectively. The multi-class random forest model's AUC-ROC values ranged from 0.80-0.95. Our models highlight how the combination of ADI and SVI increased the predictive power. This approach could reduce costs and mitigate the global public health threat posed by antibiotic-resistant infections. Machine learning techniques can predict antimicrobial-resistant infections in suspected cultures using patient data from EHRs, enabling clinicians to make targeted prescribing decisions and mitigate resistance development.
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.