AI Medical Compendium Journal:
Circulation. Cardiovascular quality and outcomes

Showing 1 to 10 of 21 articles

Prognostic Significance and Associations of Neural Network-Derived Electrocardiographic Features.

Circulation. Cardiovascular quality and outcomes Nov 14, 2024
BACKGROUND: Subtle, prognostically important ECG features may not be apparent to physicians. In the course of supervised machine learning, thousands of ECG features are identified. These are not limited to conventional ECG parameters and morphology. ...
Cardiovascular Diseases United States Middle Aged Electrocardiography Risk Assessment Unsupervised Machine Learning Time Factors Predictive Value of Tests Heart Rate Humans Neural Networks, Computer Phenotype Prognosis Risk Factors Aged Male Female Reproducibility of Results
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Artificial Intelligence-Enhanced Risk Stratification of Cancer Therapeutics-Related Cardiac Dysfunction Using Electrocardiographic Images.

Circulation. Cardiovascular quality and outcomes Sep 2, 2024
BACKGROUND: Risk stratification strategies for cancer therapeutics-related cardiac dysfunction (CTRCD) rely on serial monitoring by specialized imaging, limiting their scalability. We aimed to examine an application of artificial intelligence (AI) to...
Humans Artificial Intelligence Retrospective Studies Antineoplastic Agents Time Factors United States Electrocardiography Ventricular Function, Left Predictive Value of Tests Trastuzumab Ventricular Dysfunction, Left Stroke Volume Cardiotoxicity Middle Aged Anthracyclines Risk Assessment Female Risk Factors Aged Male
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Ethical Concerns for Remote Computer Perception in Cardiology: New Stages for Digital Health Technologies, Artificial Intelligence, and Machine Learning.

Circulation. Cardiovascular quality and outcomes May 21, 2024
Humans Cardiology Attitude to Computers Artificial Intelligence Machine Learning Digital Health Telemedicine
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Use of Artificial Intelligence Tools Across Different Clinical Settings: A Cautionary Tale.

Circulation. Cardiovascular quality and outcomes Aug 16, 2021
Humans Artificial Intelligence Deep Learning Atrial Fibrillation
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Development of a Portable Tool to Identify Patients With Atrial Fibrillation Using Clinical Notes From the Electronic Medical Record.

Circulation. Cardiovascular quality and outcomes Oct 14, 2020
BACKGROUND: The electronic medical record contains a wealth of information buried in free text. We created a natural language processing algorithm to identify patients with atrial fibrillation (AF) using text alone.
Predictive Value of Tests Natural Language Processing Atrial Fibrillation Utah Chicago Male Middle Aged Prevalence Diagnosis, Computer-Assisted Humans Female Electronic Health Records Reproducibility of Results Aged Aged, 80 and over
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Recommendations for Reporting Machine Learning Analyses in Clinical Research.

Circulation. Cardiovascular quality and outcomes Oct 14, 2020
Use of machine learning (ML) in clinical research is growing steadily given the increasing availability of complex clinical data sets. ML presents important advantages in terms of predictive performance and identifying undiscovered subpopulations of ...
Biomedical Research Data Interpretation, Statistical Periodicals as Topic Data Accuracy Machine Learning Research Design Editorial Policies Humans
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Recurrent Neural Networks for Early Detection of Heart Failure From Longitudinal Electronic Health Record Data: Implications for Temporal Modeling With Respect to Time Before Diagnosis, Data Density, Data Quantity, and Data Type.

Circulation. Cardiovascular quality and outcomes Oct 15, 2019
BACKGROUND: We determined the impact of data volume and diversity and training conditions on recurrent neural network methods compared with traditional machine learning methods.
Smoking Alcohol Drinking California Vital Signs Longitudinal Studies Predictive Value of Tests Humans Heart Failure Early Diagnosis Neural Networks, Computer Male Machine Learning Electronic Health Records Incidence Diagnosis, Computer-Assisted Female Time Factors Reproducibility of Results Primary Health Care Risk Factors
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Atrial Fibrillation Burden Signature and Near-Term Prediction of Stroke: A Machine Learning Analysis.

Circulation. Cardiovascular quality and outcomes Oct 15, 2019
BACKGROUND: Atrial fibrillation (AF) increases the risk of stroke 5-fold and there is rising interest to determine if AF severity or burden can further risk stratify these patients, particularly for near-term events. Using continuous remote monitorin...
Humans Neural Networks, Computer Machine Learning Prognosis Female Risk Factors Aged Retrospective Studies Electronic Health Records Signal Processing, Computer-Assisted Diagnosis, Computer-Assisted Stroke Time Factors Aged, 80 and over Atrial Fibrillation Logistic Models Veterans Health Services Male Proof of Concept Study United States Middle Aged Administrative Claims, Healthcare Predictive Value of Tests Risk Assessment Telemetry
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Automated and Interpretable Patient ECG Profiles for Disease Detection, Tracking, and Discovery.

Circulation. Cardiovascular quality and outcomes Sep 5, 2019
BACKGROUND: The ECG remains the most widely used diagnostic test for characterization of cardiac structure and electrical activity. We hypothesized that parallel advances in computing power, machine learning algorithms, and availability of large-scal...
Time Factors Electrocardiography Action Potentials Prognosis Predictive Value of Tests Reproducibility of Results Heart Rate Cardiovascular Diseases Humans Signal Processing, Computer-Assisted Neural Networks, Computer Markov Chains Diagnosis, Computer-Assisted Workflow Machine Learning Pattern Recognition, Automated Databases, Factual
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Intelligent Artificial Intelligence: Present Considerations and Future Implications of Machine Learning Applied to Electrocardiogram Interpretation.

Circulation. Cardiovascular quality and outcomes Sep 5, 2019
Electrocardiography Humans Artificial Intelligence Machine Learning Forecasting
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