A Machine Learning Approach for Predicting In-Hospital Cardiac Arrest Using Single-Day Vital Signs, Laboratory Test Results, and International Classification of Disease-10 Block for Diagnosis.
Journal:
Annals of laboratory medicine
PMID:
39668659
Abstract
BACKGROUND: Predicting in-hospital cardiac arrest (IHCA) is crucial for potentially reducing mortality and improving patient outcomes. However, most models, which rely solely on vital signs, may not comprehensively capture the patients' risk profiles. We aimed to improve IHCA predictions by combining vital sign indicators with laboratory test results and, optionally, International Classification of Disease-10 block for diagnosis (ICD10BD).