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Leveraging Large Language Models to Identify In-Hospital Cardiac Arrest
Jonathan Vo1, Davy Weissenbacher2, Kyndaron Reinier1
1Center for Cardiac Arrest Prevention, Smidt Heart Institute, Cedars-Sinai Health System, Los Angeles, California, USA.
None:
Manual chart abstraction is the gold standard for identifying in-hospital cardiac arrest (IHCA) but is resource intensive. Diagnosis codes are a widely used alternative given their accessibility and automated nature, but this method has poor sensitivity and positive predictive value. We present a novel large language model approach to identify IHCA and location, highlighting the potential of large language models for rapid, accurate, and automated IHCA identification from clinical notes.
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