Deep learning model for personalized prediction of positive MRSA culture using time-series electronic health records

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, PyTorchEHR, 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. PyTorchEHR outperforms logistic regression (LR) and light gradient boost machine (LGBM) models in accuracy (AUROC