Toward analyzing and synthesizing previous research in early prediction of cardiac arrest using machine learning based on a multi-layered integrative framework.
Journal:
Journal of biomedical informatics
Published Date:
Oct 30, 2018
Abstract
BACKGROUND: One of the significant problems in the field of healthcare is the low survival rate of people who have experienced sudden cardiac arrest. Early prediction of cardiac arrest can provide the time required for intervening and preventing its onset in order to reduce mortality. Traditional statistical methods have been used to predict cardiac arrest. They have often analyzed group-level differences using a limited number of variables. On the other hand, machine learning approach, which is part of a growing trend of predictive medical analysis, has provided personalized predictive analyses on more complex data and produced remarkable results.