ArticleNature communications2024
Deep learning model for personalized prediction of positive MRSA culture using time-series electronic health records.
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 20 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
20 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Global insights into MRSA bacteremia: a bibliometric analysis and future outlook.Frontiers in microbiology · 2024Pooled it
- Staphylococcus aureus-vascular endothelium interactions: Mechanisms and therapeutic opportunities.European journal of clinical microbiology & infectious diseases : official publication of the European Society of Clinical Microbiology · 2026Review
- Two-step deep-learning candidemia prediction model using two large time-sequence electronic health datasets.medRxiv : the preprint server for health sciences · 2026Article
- Artificial intelligence for improving decision-making in bacterial infection management: a narrative review.The Journal of antimicrobial chemotherapy · 2026Review
- From surveillance to intelligence: a scoping review of machine learning for antimicrobial resistance surveillance intelligence across One Health.Frontiers in public health · 2026Article
- Opportunities for AI-based Model-informed Drug Development: A Comparative Analysis of NONMEM and AI-based Models for Population Pharmacokinetic Prediction.The AAPS journal · 2025Article
- Transparent AI-driven personalized risk prediction system for acute kidney injury after total hip arthroplasty.Scientific reports · 2025Article
- Artificial Intelligence in the Management of Infectious Diseases in Older Adults: Diagnostic, Prognostic, and Therapeutic Applications.Biomedicines · 2025Review
- Role of artificial intelligence in ICU therapeutic decision-making for severe infections.Current opinion in critical care · 2025Review
- Learning-based early detection of post-hepatectomy liver failure using temporal perioperative data: a nationwide multicenter retrospective study in China.EClinicalMedicine · 2025Article
- Explainable Machine Learning Model for Predicting Persistent Sepsis-Associated Acute Kidney Injury: Development and Validation Study.Journal of medical Internet research · 2025Article
- Generative AI Models in Time-Varying Biomedical Data: Scoping Review.Journal of medical Internet research · 2025Article
- Antibiotics and Artificial Intelligence: Clinical Considerations on a Rapidly Evolving Landscape.Infectious diseases and therapy · 2025Article
- The Role of Artificial Intelligence and Machine Learning Models in Antimicrobial Stewardship in Public Health: A Narrative Review.Antibiotics (Basel, Switzerland) · 2025Review
- ClpB affects biofilm formation in methicillin-resistantFrontiers in microbiology · 2025Article
- The role of artificial intelligence and machine learning in predicting and combating antimicrobial resistance.Computational and structural biotechnology journal · 2025Review
- BOBM: an adaptive deep learning framework for extended-window sepsis prediction with cross-institutional generalizability.Frontiers in medicine · 2025Article
- Risk Factors for Methicillin-ResistantThe Canadian journal of infectious diseases & medical microbiology = Journal canadien des maladies infectieuses et de la microbiologie medicale · 2025Article
- Knowledge graph driven medicine recommendation system using graph neural networks on longitudinal medical records.Scientific reports · 2024Article
- Affordable and real-time antimicrobial resistance prediction from multimodal electronic health records.Scientific reports · 2024Article
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.
Funding
Abstract
Methicillin-resistant Staphylococcus aureus (MRSA) poses significant morbidity and mortality in hospitals. Rapid, accurate risk stratification of MRSA is crucial for optimizing antibiotic therapy. Our study introduced a deep learning model, PyTorch_EHR, which leverages electronic health record (EHR) time-series data, including wide-variety patient specific data, to predict MRSA culture positivity within two weeks. 8,164 MRSA and 22,393 non-MRSA patient events from Memorial Hermann Hospital System, Houston, Texas are used for model development. PyTorch_EHR outperforms logistic regression (LR) and light gradient boost machine (LGBM) models in accuracy (AUROC
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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.