Machine Learning Improves the Identification of Individuals With Higher Morbidity and Avoidable Health Costs After Acute Coronary Syndromes

Traditional risk scores improved the definition of the initial therapeutic strategy in acute coronary syndrome (ACS), but they were not designed for predicting long-term individual risks and costs. In parallel, attempts to directly predict costs from clinical variables in ACS had limited success. Thus, novel approaches to predict cardiovascular risk and health expenditure are urgently needed. Our objectives were to predict the risk of major/minor adverse cardiovascular events (MACE) and estimate assistance-related costs.

Source: Value in Health - October 12, 2020 Category: International Medicine & Public Health Authors: Luiz S érgio Fernandes de Carvalho, Silvio Gioppato, Marta Duran Fernandez, Bernardo Carvalho Trindade, José Carlos Quinaglia e Silva, Rebeca Gouget Sérgio Miranda, José Roberto Matos de Souza, Wilson Nadruz, Sandra Eliza Fontes Avila, Andrei Carvalho Source Type: research