AI Medical Compendium Journal:
Journal of the American Heart Association

Showing 51 to 60 of 64 articles

Deep Learning-Based Algorithm for Detecting Aortic Stenosis Using Electrocardiography.

Journal of the American Heart Association Mar 21, 2020
Background Severe, symptomatic aortic stenosis (AS) is associated with poor prognoses. However, early detection of AS is difficult because of the long asymptomatic period experienced by many patients, during which screening tools are ineffective. The...
Humans Retrospective Studies Republic of Korea Female Reproducibility of Results Aged Electrocardiography Aortic Valve Stenosis Aortic Valve Predictive Value of Tests Signal Processing, Computer-Assisted Early Diagnosis Aged, 80 and over Male Middle Aged Severity of Illness Index Deep Learning
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Machine Learning Framework to Identify Individuals at Risk of Rapid Progression of Coronary Atherosclerosis: From the PARADIGM Registry.

Journal of the American Heart Association Feb 22, 2020
Background Rapid coronary plaque progression (RPP) is associated with incident cardiovascular events. To date, no method exists for the identification of individuals at risk of RPP at a single point in time. This study integrated coronary computed to...
Humans Machine Learning Prospective Studies Female Coronary Artery Disease Multidetector Computed Tomography Plaque, Atherosclerotic Computed Tomography Angiography Aged Time Factors Male Predictive Value of Tests Middle Aged Coronary Angiography Disease Progression Radiographic Image Interpretation, Computer-Assisted Diagnosis, Computer-Assisted Registries
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State-of-the-Art Machine Learning Techniques Aiming to Improve Patient Outcomes Pertaining to the Cardiovascular System.

Journal of the American Heart Association Feb 13, 2020
Heart Disease Risk Factors Humans Quality Improvement Machine Learning Quality Indicators, Health Care Precision Medicine Therapy, Computer-Assisted Cardiovascular Diseases Outcome and Process Assessment, Health Care Diagnosis, Computer-Assisted Treatment Outcome Data Mining
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Novel Screening Method Identifies PI3Kα, mTOR, and IGF1R as Key Kinases Regulating Cardiomyocyte Survival.

Journal of the American Heart Association Oct 16, 2019
Background Small molecule kinase inhibitors (KIs) are a class of agents currently used for treatment of various cancers. Unfortunately, treatment of cancer patients with some of the KIs is associated with cardiotoxicity, and there is an unmet need fo...
Machine Learning Cells, Cultured Animals Rats Cell Survival Protein Kinase Inhibitors Cardiotoxins Receptor, IGF Type 1 Phosphatidylinositol 3-Kinases TOR Serine-Threonine Kinases Adenosine Triphosphate Myocytes, Cardiac
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Artificial Intelligence: Practical Primer for Clinical Research in Cardiovascular Disease.

Journal of the American Heart Association Aug 27, 2019
Therapy, Computer-Assisted Biomedical Research Cardiovascular Diseases Diagnosis, Computer-Assisted Telemedicine Diffusion of Innovation Humans Artificial Intelligence Precision Medicine
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Bioimpedance and New-Onset Heart Failure: A Longitudinal Study of >500 000 Individuals From the General Population.

Journal of the American Heart Association Jun 29, 2018
BACKGROUND: Heart failure constitutes a high burden on patients and society, but although lifetime risk is high, it is difficult to predict without costly or invasive testing. We aimed to establish new risk factors of heart failure, which potentially...
Risk Assessment Incidence Time Factors Longitudinal Studies Age Factors Body Composition Myocardial Infarction Heart Failure Sex Factors Electric Impedance United Kingdom Leg Female Humans Risk Factors Machine Learning Aged Decision Support Techniques Male Prospective Studies Middle Aged Prognosis
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An Algorithm Based on Deep Learning for Predicting In-Hospital Cardiac Arrest.

Journal of the American Heart Association Jun 26, 2018
BACKGROUND: In-hospital cardiac arrest is a major burden to public health, which affects patient safety. Although traditional track-and-trigger systems are used to predict cardiac arrest early, they have limitations, with low sensitivity and high fal...
Seoul Middle Aged Risk Assessment Diagnosis, Computer-Assisted Deep Learning Time Factors Inpatients Predictive Value of Tests Early Diagnosis Vital Signs Heart Arrest Resuscitation Female Humans Decision Support Techniques Reproducibility of Results Prognosis Risk Factors Retrospective Studies Aged Adult Male
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Machine Learning Methods Improve Prognostication, Identify Clinically Distinct Phenotypes, and Detect Heterogeneity in Response to Therapy in a Large Cohort of Heart Failure Patients.

Journal of the American Heart Association Apr 12, 2018
BACKGROUND: Whereas heart failure (HF) is a complex clinical syndrome, conventional approaches to its management have treated it as a singular disease, leading to inadequate patient care and inefficient clinical trials. We hypothesized that applying ...
Sweden Aged Aged, 80 and over Heart Failure Humans Ventricular Function, Left Survival Rate Diuretics Male Machine Learning Stroke Volume Angiotensin Receptor Antagonists Phenotype Algorithms Middle Aged Angiotensin-Converting Enzyme Inhibitors Prognosis Follow-Up Studies Cardiovascular Agents Retrospective Studies Registries Adrenergic beta-Antagonists Female Reproducibility of Results
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Association Between Genotypes and Risk of Cardiovascular Disease in MESA (Multi-Ethnic Study of Atherosclerosis).

Journal of the American Heart Association Dec 21, 2017
BACKGROUND: genetic variants confer an increased risk for kidney disease. Their associations with cardiovascular disease (CVD) are less certain. We aimed to compare the prevalence of subclinical CVD and incidence of atherosclerotic CVD and heart fai...
Incidence Cross-Sectional Studies DNA Mutational Analysis Genotype Male Humans Cardiovascular Diseases Female DNA Middle Aged Apolipoprotein L1 Risk Factors Atherosclerosis Prevalence Mutation, Missense Aged Ethnicity Aged, 80 and over
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Determinants of In-Hospital Mortality After Percutaneous Coronary Intervention: A Machine Learning Approach.

Journal of the American Heart Association Mar 5, 2019
Background The ability to accurately predict the occurrence of in-hospital death after percutaneous coronary intervention is important for clinical decision-making. We sought to utilize the New York Percutaneous Coronary Intervention Reporting System...
Hospital Mortality Treatment Outcome Age Factors Registries Percutaneous Coronary Intervention Coronary Artery Disease Risk Factors Data Mining Aged New York Male Stroke Volume Middle Aged Humans Machine Learning Databases, Factual Female
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