AI-Driven Decision Support for Early Detection of Cardiac Events: Unveiling Patterns and Predicting Myocardial Ischemia.
Luís Brito ElvasMiguel NunesJoão Carlos FerreiraMiguel Sales DiasLuís Brás RosárioPublished in: Journal of personalized medicine (2023)
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.
Keyphrases
- data analysis
- left ventricular
- aortic stenosis
- cardiovascular disease
- machine learning
- big data
- electronic health record
- healthcare
- decision making
- transcatheter aortic valve replacement
- artificial intelligence
- ejection fraction
- aortic valve replacement
- transcatheter aortic valve implantation
- primary care
- aortic valve
- heart failure
- pulmonary hypertension
- patient safety
- physical activity
- emergency department
- risk factors
- type diabetes
- coronary artery disease
- adipose tissue
- insulin resistance
- metabolic syndrome
- quality improvement