AIMC Topic: Heart Failure

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Detecting cardiac pathologies via machine learning on heart-rate variability time series and related markers.

Scientific reports
In this paper we develop statistical algorithms to infer possible cardiac pathologies, based on data collected from 24 h Holter recording over a sample of 2829 labelled patients; labels highlight whether a patient is suffering from cardiac pathologie...

A Machine Learning Approach to Management of Heart Failure Populations.

JACC. Heart failure
BACKGROUND: Heart failure is a prevalent, costly disease for which new value-based payment models demand optimized population management strategies.

Video-based AI for beat-to-beat assessment of cardiac function.

Nature
Accurate assessment of cardiac function is crucial for the diagnosis of cardiovascular disease, screening for cardiotoxicity and decisions regarding the clinical management of patients with a critical illness. However, human assessment of cardiac fun...

Identifying Cancer Patients at Risk for Heart Failure Using Machine Learning Methods.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Cardiotoxicity related to cancer therapies has become a serious issue, diminishing cancer treatment outcomes and quality of life. Early detection of cancer patients at risk for cardiotoxicity before cardiotoxic treatments and providing preventive mea...

Continuous Wearable Monitoring Analytics Predict Heart Failure Hospitalization: The LINK-HF Multicenter Study.

Circulation. Heart failure
BACKGROUND: Implantable cardiac sensors have shown promise in reducing rehospitalization for heart failure (HF), but the efficacy of noninvasive approaches has not been determined. The objective of this study was to determine the accuracy of noninvas...

Exercise cardiac power and the risk of heart failure in men: A population-based follow-up study.

Journal of sport and health science
BACKGROUND: Little is known about exercise cardiac power (ECP), defined as the ratio of directly measured maximal oxygen uptake with peak systolic blood pressure during exercise, on heart failure (HF) risk. We examined the association of ECP and the ...

Machine learning for predicting cardiac events: what does the future hold?

Expert review of cardiovascular therapy
: With the increase in the number of patients with cardiovascular diseases, better risk-prediction models for cardiovascular events are needed. Statistical-based risk-prediction models for cardiovascular events (CVEs) are available, but they lack the...

Detecting Congestive Heart Failure by Extracting Multimodal Features and Employing Machine Learning Techniques.

BioMed research international
The adaptability of heart to external and internal stimuli is reflected by the heart rate variability (HRV). Reduced HRV can be a predictor of negative cardiovascular outcomes. Based on the nonlinear, nonstationary, and highly complex dynamics of the...