Abstract:

Newborn cardiac arrest is a frightening yet common medical emergency. To give these infants the greatest care and treatment possible, early identification is essential. The development of precise and effective diagnostic instruments for early diagnosis as well as the identification of possible biomarkers and indications of cardiac arrest in neonates have been the main topics of recent study. A variety of imaging methods, including computed tomography and echocardiography, may aid in the early identification of cardiac arrest. The objective of this study is to use statistical models to create a Cardiac Machine Learning model (CMLM) for the early diagnosis of cardiac arrest in neonates in the Cardiac Intensive Care Unit (CICU). The neonate's physiological data were combined to identify the cardiac arrest occurrences.

Description:

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