AI-based mobile applications for public health response

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AI-driven decision support for early detection of cardiac events: unveiling patterns and predicting myocardial ischemia

Publication . Elvas, Luís B.; Nunes, Miguel; Ferreira, Joao C.; Dias, Miguel Sales; Rosario, Luis

Cardiovascular diseases (CVDs) account for a significant portion of global mortality, emphasizing the need for effective strategies. This study focuses on myocardial infarction, pulmonary thromboembolism, and aortic stenosis, aiming to empower medical practitioners with tools for informed decision making and timely interventions. Drawing from data at Hospital Santa Maria, our approach combines exploratory data analysis (EDA) and predictive machine learning (ML) models, guided by the Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology. EDA reveals intricate patterns and relationships specific to cardiovascular diseases. ML models achieve accuracies above 80%, providing a 13 min window to predict myocardial ischemia incidents and intervene proactively. This paper presents a Proof of Concept for real-time data and predictive capabilities in enhancing medical strategies.

2023Artigo científico

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Entidade financiadora

Fundação para a Ciência e a Tecnologia

Programa de financiamento

3599-PPCDT

Número da atribuição

DSAIPA/AI/0122/2020