Performance of a large language model for identifying central line-associated bloodstream infections (CLABSI) using real clinical notes
Guillermo Rodriguez‐Nava, Goar Egoryan, Katherine E Goodman, Deborah Morgan, Jorge Salinas
Abstract
We evaluated one of the first secure large language models approved for protected health information, for identifying central line-associated bloodstream infections (CLABSIs) using real clinical notes. Despite no pretraining, the model demonstrated rapid assessment and high sensitivity for CLABSI identification. Performance would improve with access to more patient data.
Topics & Concepts
Central lineBloodstream infectionMedicineIdentification (biology)Intensive care medicineBiologyBotanyArtificial Intelligence in Healthcare and EducationElectronic Health Records SystemsCOVID-19 diagnosis using AI