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A publicação pode ser exportada nos seguintes formatos: referência da APA (American Psychological Association), referência do IEEE (Institute of Electrical and Electronics Engineers), BibTeX e RIS.

Exportar Referência (APA) 
Elvas, L. B., Nunes, M., Ferreira, J. C., Dias, M. S. & Rosário, L. B. (2023). AI-driven decision support for early detection of cardiac events: Unveiling patterns and predicting myocardial ischemia. Journal of Personalized Medicine. 13 (9)
Exportar Referência (IEEE) 
L. M. Elvas et al.,  "AI-driven decision support for early detection of cardiac events: Unveiling patterns and predicting myocardial ischemia", in Journal of Personalized Medicine, vol. 13, no. 9, 2023
Exportar BibTeX 
@article{elvas2023_1764926932082,
	author = "Elvas, L. B. and Nunes, M. and Ferreira, J. C. and Dias, M. S. and Rosário, L. B.",
	title = "AI-driven decision support for early detection of cardiac events: Unveiling patterns and predicting myocardial ischemia",
	journal = "Journal of Personalized Medicine",
	year = "2023",
	volume = "13",
	number = "9",
	doi = "10.3390/jpm13091421",
	url = "https://www.mdpi.com/journal/jpm"
}
Exportar RIS 
TY  - JOUR
TI  - AI-driven decision support for early detection of cardiac events: Unveiling patterns and predicting myocardial ischemia
T2  - Journal of Personalized Medicine
VL  - 13
IS  - 9
AU  - Elvas, L. B.
AU  - Nunes, M.
AU  - Ferreira, J. C.
AU  - Dias, M. S.
AU  - Rosário, L. B.
PY  - 2023
SN  - 2075-4426
DO  - 10.3390/jpm13091421
UR  - https://www.mdpi.com/journal/jpm
AB  - 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.
ER  -