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JCSIS

Machine Learning-Driven Clinical Decision Support System for Antibiotic Resistance Pattern Identification.

Weiguo Gee
Volume 5

Abstract

Antibiotic resistance has become one of the most serious global threats to public health, leading to increased treatment failure, prolonged hospitalization, higher healthcare costs, and elevated mortality rates. The rapid emergence of multidrug-resistant bacterial strains has created an urgent need for intelligent clinical decision support systems capable of identifying resistance patterns and assisting clinicians in selecting appropriate antimicrobial therapy. Conventional antibiotic prescribing often relies on empirical treatment guidelines, local antibiograms, and delayed laboratory culture results, which may be insufficient for rapidly changing resistance trends and patient-specific risk factors. To address these challenges, this paper proposes a machine learning-driven Clinical Decision Support System (CDSS) for antibiotic resistance pattern identification using microbiological, clinical, and electronic health record data.

The proposed framework integrates structured patient information, including demographic variables, infection site, prior antibiotic exposure, hospitalization history, comorbidities, laboratory findings, microbiology culture results, antimicrobial susceptibility testing profiles, pathogen species, intensive care admission status, device use, and previous colonization or infection with resistant organisms. A comprehensive preprocessing pipeline is applied to clean and harmonize heterogeneous clinical data, handle missing values, encode categorical variables, normalize numerical features, remove duplicate culture records, and align microbiology reports with patient-level clinical timelines. To improve predictive reliability, the framework also incorporates feature selection and temporal aggregation strategies that capture recent antimicrobial exposure, recurrent infections, and hospital-acquired risk factors.

Several machine learning models are developed and compared, including Logistic Regression, Random Forest, Support Vector Machine, XGBoost, LightGBM, CatBoost, and deep neural network architectures. These models are trained to identify resistance patterns for clinically important pathogens and antimicrobial classes, including methicillin-resistant Staphylococcus aureus, vancomycin-resistant Enterococcus, extended-spectrum beta-lactamase-producing Enterobacterales, carbapenem-resistant organisms, multidrug-resistant Pseudomonas aeruginosa, and resistant Acinetobacter baumannii. The proposed CDSS generates patient-specific resistance probability scores and provides antimicrobial risk stratification to support empirical therapy selection before definitive susceptibility results become available.

To address class imbalance caused by the relatively low prevalence of certain resistant organisms, the framework incorporates class-weighted loss functions, focal loss, synthetic minority oversampling, and stratified cross-validation. Model performance is evaluated using accuracy, sensitivity, specificity, precision, F1-score, AUC-ROC, AUC-PR, calibration analysis, and decision-curve analysis. Particular emphasis is placed on sensitivity and negative predictive value, as failure to identify resistant infections may result in inappropriate therapy and adverse clinical outcomes. Comparative evaluation is conducted against conventional antibiogram-based recommendations, rule-based clinical protocols, and non-personalized empirical prescribing strategies.

To enhance interpretability and clinical trust, the proposed system integrates explainable artificial intelligence techniques such as SHAP, permutation feature importance, and patient-level risk attribution. These methods identify the most influential factors contributing to predicted resistance, including prior antibiotic exposure, recent hospitalization, ICU stay, invasive devices, recurrent infection history, renal dysfunction, immunosuppression, and previous resistant organism isolation. By presenting both global resistance trends and individualized explanations, the CDSS supports antimicrobial stewardship teams, infectious disease specialists, and frontline clinicians in making evidence-based prescribing decisions.

The proposed machine learning-driven CDSS provides a scalable, interpretable, and clinically actionable approach for antibiotic resistance pattern identification. By combining microbiology data, EHR-derived patient risk factors, and predictive analytics, the framework can improve early recognition of resistant infections, reduce inappropriate antibiotic use, support antimicrobial stewardship programs, and contribute to limiting the spread of antimicrobial resistance. Its integration into hospital information systems and laboratory workflows may enable real-time decision support, personalized empirical therapy selection, and improved patient safety in both inpatient and emergency care settings.

 

Keywords

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