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

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^PyTorch_EHR = 0.911, AUROC^LR = 0.857, AUROC^LGBM = 0.892). External validation with 393,713 patient even

SUBMITTER: Nigo M 

PROVIDER: S-EPMC10917736 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature