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. 2023 Sep 21;13(9):1421.
doi: 10.3390/jpm13091421.

AI-Driven Decision Support for Early Detection of Cardiac Events: Unveiling Patterns and Predicting Myocardial Ischemia

Luís B Elvas  1   2 , Miguel Nunes  1 , Joao C Ferreira  1   2 , Miguel Sales Dias  1 , Luís Brás Rosário  3
Affiliations

AI-Driven Decision Support for Early Detection of Cardiac Events: Unveiling Patterns and Predicting Myocardial Ischemia

Luís B Elvas et al. J Pers Med. .

Abstract

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.

Keywords: aortic stenosis; artificial intelligence; cardiovascular diseases; data mining; exploratory data analysis; machine learning; myocardial infarction; prediction; pulmonary thromboembolism; stenosis cardiology.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
PRISMA workflow diagram.
Figure 2
Figure 2
Average Heart Rate by time and gender.
Figure 3
Figure 3
Average Heart Rate in the initial 4 days of admission by daytime and nighttime period.
Figure 4
Figure 4
Average Heart Rate in the initial 24 h of admission.
Figure 5
Figure 5
Average Respiration Rate for first 24 h of admission.
Figure 6
Figure 6
Average Troponin values by day of admission.
Figure 7
Figure 7
Average Heart Rate by time and gender.
Figure 8
Figure 8
Average Heart Rate in the initial 4 days of admission by daytime and nighttime periods.
Figure 9
Figure 9
Average Heart Rate in the initial 24 h of admission.
Figure 10
Figure 10
Average Respiration Rate for first 24 h of admission.
Figure 11
Figure 11
Average Troponin values by day of admission.
Figure 12
Figure 12
Average Heart Rate by time and gender.
Figure 13
Figure 13
Average Heart Rate in the initial 4 days of admission by daytime and nighttime periods.
Figure 14
Figure 14
Average Heart Rate in the initial 24 h of admission.
Figure 15
Figure 15
Average Respiration Rate for first 24 h of admission.
Figure 16
Figure 16
Average Troponin value by day of admission.
Figure 17
Figure 17
Here we represent as an example the autocorrelation plots for the variables myocardial ischemia and ST Segment Lead AVF, where (a) represents the autocorrelation for myocardial ischemia and (b) represents the autocorrelation for ST segment Lead AVF.
See this image and copyright information in PMC

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