Published Paper Details:

Cardiac arrests are critical medical events that constitute a major cause of death in many industrialized countries. Timely and appropriate detection and treatment are essential for the survival of an individual suffering from a cardiac arrest. Recent advances in the field of artificial intelligence and machine learning techniques have enabled the development of effective early detection models of cardiac arrests. Machine learning algorithms use data-driven modeling approaches to capture and analyze patterns in both clinical data and in streams of physiological signals. These systems are capable of extracting important features and subtle changes in patient physiology that may not have been captured using traditional monitoring methods. This review presents an overview of the various machine learning approaches proposed for the early detection of cardiac arrests, their strengths and weaknesses, and how these approaches relate to existing standards of care. It also provides perspectives for future research directions and provides insights into the potential of machine learning technology for improving patient outcomes