ArticleInfection and drug resistance2025
Machine Learning-Based Prediction Model for Multidrug-Resistant Organisms Infections: Performance Evaluation and Interpretability Analysis.
Article in Infection and drug resistance, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Recent advancements in artificial intelligence applications for the mitigation of antimicrobial resistance: challenges and opportunities.JAC-antimicrobial resistance · 2026Review
- Interpretable machine learning for predicting in-hospital mortality in COPD ICU patients: a rigorous validation across time and geography.Respiratory research · 2026Article
- Can machine learning support infection control measures by predicting carbapenemase-producing Enterobacterales colonization at admission?Infection control and hospital epidemiology · 2026Article
- Development and internal-external validation of a machine learning-based risk prediction model for multidrug resistance in mechanically ventilated ICU patients with chronic obstructive pulmonary disease.Frontiers in medicine · 2026Article
- Ten key considerations for developing interpretable risk prediction models.Frontiers in bioinformatics · 2026Article
- Simultaneous assessment of stress hyperglycemia ratio and glucose variability to predict all-cause mortality in sepsis patients across different glucose metabolic states: an observational cohort study with interpretable machine learning approach.International journal of surgery (London, England) · 2026Observational
- From surveillance to intelligence: a scoping review of machine learning for antimicrobial resistance surveillance intelligence across One Health.Frontiers in public health · 2026Article
- Predicting Multidrug-Resistant Pneumonia: An Interpretable Machine Learning Model Validated in US and Chinese Patient Cohorts.Infection and drug resistance · 2026Article
- Early risk stratification for carbapenem resistance amongFrontiers in cellular and infection microbiology · 2026Article
- Explainable Machine Learning Integrating Patient and Environmental Factors for Predicting Multidrug-Resistant Organism Colonization or Infection on ICU Admission.Infection and drug resistance · 2026Article
- Development and validation of an interpretable machine learning model for predicting multidrug-resistant infections in ICU patients.Frontiers in cellular and infection microbiology · 2026Article
- Pathogen characteristics and pre-index clinical factors associated with multidrug-resistant organism infections in intensive care unit patients.Frontiers in public health · 2026Observational
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Multidrug-resistant organism (MDRO) infections pose a significant global health threat, particularly in intensive care units (ICUs), where delayed identification exacerbates clinical outcomes. Although machine learning (ML) holds promise for infection prediction, the opaque nature of complex algorithms impedes clinical adoption. This study evaluated an interpretable machine learning model incorporating SHapley Additive exPlanations (SHAP) to predict MDRO infections in ICU patients. Methods: A retrospective cohort study was conducted on 888 ICU patients (2020-2022) from a tertiary hospital in China. Following TRIPOD guidelines, key predictors were identified using Lasso regression from a comprehensive set of clinical variables, including demographics, treatments, and laboratory data. Six machine learning algorithms-Neural Networks, Random Forests, Support Vector Machines, Logistic Regression, Decision Trees, and Gaussian Naive Bayes-were evaluated based on AUC, accuracy, and calibration curves. SHAP analysis provided both global and local interpretability. Results: Among 825 eligible cases (375 MDRO infections), the Random Forest model exhibited the highest performance (AUC = 0.83, accuracy = 76.7%). SHAP analysis identified urinary catheterization, ventilator use, and prolonged antibiotic exposure as key modifiable risk factors. Case-level interpretation via dynamic force plots illustrated individualized risk stratification. Decision curve analysis indicated clinical utility within probability thresholds of 0.44-0.60. Conclusion: This study establishes an interpretable prediction framework integrating RF algorithms with SHAP explainability, balancing predictive accuracy with clinical transparency. The model's dynamic visualization capabilities support individualized risk assessment and evidence-based antimicrobial stewardship. Integration into hospital information systems with real-time dashboards could enhance early intervention strategies.
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