Integrating socioeconomic deprivation indices and electronic health record data to predict antimicrobial resistance

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.

Fig. 1. Combined AUC ROC for binary classification models.

The combined area under the receiver operating characteristics curves (AUC ROC) for the logistic regression, random forest, and XGBoost models with the logistic regression represented by the dark blue line, random forest with the cyan line, and XGBoost with the magenta line.

Fig. 2

Feature importance for Random Forest Model.

Fig. 3. AUC ROC for multi-class classification of 5 AMR organisms of interest.

AUC ROC plots for the three described models: logistic regression, random forest, and XGBoost. a ROC-AUC curve of ESBL; b ROC-AUC curve of CRE; c ROC-AUC curve of VRE; d ROC-AUC curve of MRSA; e ROC-AUC curve of AmpC. For each plot, the AUC ROCs for each model of interest are shown with the following color palette: a cyan solid line for Random Forest model, a light purple solid line for Logistic Regression, a pink solid line for XGBoost.