Faculty, Staff and Student Publications
Publication Date
3-6-2024
Journal
Nature Communications
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
Keywords
Humans, Deep Learning, Electronic Health Records, Methicillin-Resistant Staphylococcus aureus, Critical Care, Hospitals
Included in
Biomedical Informatics Commons, Health and Medical Administration Commons, Health Information Technology Commons
Comments
Supplementary Materials
PMID: 38448409