ArticleNature communications2024
Personalising intravenous to oral antibiotic switch decision making through fair interpretable machine learning.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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
16 citing papers in PubMed, 1 synthesis or guideline pooled it, 29 citations in OpenAlex.
- Artificial intelligence for infection surveillance, risk stratification, and antimicrobial decision support in acute-care hospitals: a scoping review.Frontiers in artificial intelligence · 2026Pooled it
- Optimising antibiotic switching via forecasting of patient physiology.Nature communications · 2026Article
- Performance of predictive AI-based clinical decision support systems across clinical domains: A systematic review and meta-analysis.PLOS digital health · 2026Article
- Artificial intelligence for improving decision-making in bacterial infection management: a narrative review.The Journal of antimicrobial chemotherapy · 2026Review
- A mixed methods evaluation of an antimicrobial prescribing clinical decision support system app.npj antimicrobials and resistance · 2025Article
- Internal Validation of a Machine Learning-Based CDSS for Antimicrobial Stewardship.Life (Basel, Switzerland) · 2025Article
- Leveraging Hospital Information Data for Effective Antibiotic Stewardship.Journal of Korean medical science · 2025Review
- A Precision Medicine Model for Targeted Antibiotic Therapy in Urinary Tract Infections: A Valuable Tool to Reduce Hospitalization Stay and the Time to Switch to Oral Treatment.Antibiotics (Basel, Switzerland) · 2025Article
- Artificial intelligence in hospital infection prevention: an integrative review.Frontiers in public health · 2025Review
- Can we rely on artificial intelligence to guide antimicrobial therapy? A systematic literature review.Antimicrobial stewardship & healthcare epidemiology : ASHE · 2025Article
- Predicting individual patient and hospital-level discharge using machine learning.Communications medicine · 2024Article
- Advancing Patient Safety: The Future of Artificial Intelligence in Mitigating Healthcare-Associated Infections: A Systematic Review.Healthcare (Basel, Switzerland) · 2024Review
- Tackling the Antimicrobial Resistance "Pandemic" with Machine Learning Tools: A Summary of Available Evidence.Microorganisms · 2024Review
- Personalising intravenous to oral antibiotic switch decision making through fair interpretable machine learning.Nature communications · 2024Article
- A machine learning-based predictive model of causality in orthopaedic medical malpractice cases in China.PloS one · 2024Article
- Role of artificial intelligence in bacterial diagnostics and surveillance of anti-microbial resistance.Digital healthReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Antimicrobial resistance (AMR) and healthcare associated infections pose a significant threat globally. One key prevention strategy is to follow antimicrobial stewardship practices, in particular, to maximise targeted oral therapy and reduce the use of indwelling vascular devices for intravenous (IV) administration. Appreciating when an individual patient can switch from IV to oral antibiotic treatment is often non-trivial and not standardised. To tackle this problem we created a machine learning model to predict when a patient could switch based on routinely collected clinical parameters. 10,362 unique intensive care unit stays were extracted and two informative feature sets identified. Our best model achieved a mean AUROC of 0.80 (SD 0.01) on the hold-out set while not being biased to individuals protected characteristics. Interpretability methodologies were employed to create clinically useful visual explanations. In summary, our model provides individualised, fair, and interpretable predictions for when a patient could switch from IV-to-oral antibiotic treatment. Prospectively evaluation of safety and efficacy is needed before such technology can be applied clinically.
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.