Myocardial Infarction

Myocardial infarction (MI), commonly known as a heart attack is the disease of the blood vessels supplying the heart muscle (Myocardium) i.e. coronary heart disease. The area of heart muscle that has either zero flow or so little flow that it cannot sustain cardiac muscle function is said to be infracted and the overall process is called a myocardial infarction. MI are of two types; transmural and subendocardial. Mainly it is caused due to oxidative stress and atherosclerosis.Chest pain is the most common symptom of acute MI and is often described as a sensation of tightness, pressure, or squeezing. Other symptoms include diaphoresis (an excessive form of sweating), Shortness of breath (dyspnea), weakness, light-headedness, nausea, vomiting, and palpitations. The most common symptoms of MI in women include dyspnea, weakness, and fatigue, sleep disturbances. It can be treated by using blockers, diuretics, ACE inhibitors, calcium channel blockers and nitrates.

Cardiovascular Applications of Artificial Intelligence in Research, Diagnosis, and Disease Management

Despite significant advancements in diagnosis and disease management, cardiovascular (CV) disorders remain the No. 1 killer both in the United States and across the world, and innovative and transformative technologies such as artificial intelligence (AI) are increasingly employed in CV medicine. In this chapter, the authors introduce different AI and machine learning (ML) tools including support vector machine (SVM), gradient boosting machine (GBM), and deep learning models (DL), and their applicability to advance CV diagnosis and disease classification, and risk prediction and patient management. The applications include, but are not limited to, electrocardiogram, imaging, genomics, and drug research in different CV pathologies such as myocardial infarction (heart attack), heart failure, congenital heart disease, arrhythmias, valvular abnormalities, etc.

KNN Algorithm Used for Heart Attack Detection

Machine Learning, a branch of artificial intelligence, has become more accurate than human medical professionals in predicting the incidence of heart attack or death in patients at risk of coronary artery disease. In this paper, we attempt to employ Artificial Intelligence (AI) to predict heart attack. For this purpose, we employed the popular classification technique named the K-Nearest Neighbor (KNN) algorithm to predict the probability of having the Heart Attack (HA). The dataset used is the cardiovascular dataset available publicly on Kaggle, knowing that someone suffering from cardiovascular disease is likely to succumb to a heart attack. In this work, the research was conducted using two approaches. We use the KNN classifier for the first time, aided by using a correlation matrix to select the best features manually and faster computation, and then optimize the parameters with the K-fold cross-validation technique. This improvement led us to have an accuracy of 72.37% on the test set.

The Long Arm of Prospective Childhood Income for Mature Adult Health in the U.S.

Pioneering scholarship links retrospective childhood conditions to mature adult health. We distinctively provide critical evidence with prospective state-of-the-art measures of parent income observed multiple times during childhood in the 1970s-1990s. Using the Panel Study of Income Dynamics, we analyze six health outcomes (self-rated health, heart attack, stroke, life-threatening chronic conditions, non-life-threatening chronic conditions, and psychological distress) among 40-65 year olds (N=3,813-3,944). Parent relative income rank has statistically and substantively significant relationships with five of six outcomes. The relationships with heart attack, stroke and life threatening chronic conditions are particularly strong. Parent income rank performs slightly better than alternative prospective and retrospective measures. At the same time, we provide novel validation on which retrospective measures (i.e. father’s education) perform almost as well as prospective measures. Further, we inform several perennial debates about how relative versus absolute income and other measures of socio-economic status and social class influence health.

A Mixed-Methods Study of the Impact of Mild Cognitive Impairment Diagnosis on Patient and Care Partner Perception of Health Risks

Background: Older patients (≥65 years) with mild cognitive impairment (MCI) are undertreated for cardiovascular disease (CVD). One reason for this disparity could be that patients with MCI might underestimate the chances of CVD and overestimate dementia. Objective: To compare conceptions of health risk between older patients with MCI and normal cognition (NC) and their care partners. Methods: We conducted a multi-center mixed-methods study of patient-care partner dyads completing written quantitative surveys (73% response rate; 127 dyads: 66 MCI and 61 NC) or semi-structured interviews (20 dyads: 11 MCI, and 9 NC). Surveys assessed two-year patient risks of dementia, heart attack, stroke, and fall. Interviews assessed similar health risks and reasons for risk perceptions. Results: On surveys, a similarly low proportion of MCI and NC patients felt they were at risk of stroke (5% versus 2% ; p = 0.62) and heart attack (2% versus 0% ; p = 0.99). More MCI than NC patients perceived dementia risk (26% versus 2% ; p < 0.001). Care partners’ survey findings were similar. Interviews generally confirmed these patterns and also identified reasons for future health concerns. For both MCI and NC dyads, personal experience with cognitive decline or CVD (personal or family history) increased concerns about each disease. Additionally, perceptions of irreversibility and lack of treatment for cognitive decline increased concern about dementia. Conclusion: Less use of CVD treatments in MCI seems unlikely to be driven by differential perceptions of CVD risk. Future work to improve awareness of CVD risks in older patients and dementia risk in patients with MCI are warranted.