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Heart Failure

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Neural Networks for Prognostication of Patients With Heart Failure.

Circulation. Heart failure
Background Prognostication of heart failure patients from cardiopulmonary exercise test (CPET) currently involves simplification of complex time-series data into summary indices. We hypothesized that prognostication could be improved by considering t...

CHF Detection with LSTM Neural Network.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Heart rate variability has been proven to be an effective prediction of risk of heart failure. The tradition method required manual feature extraction, thus may lead to potential error. In order to improve the robustness, a deep learning method based...

Using Unsupervised Machine Learning to Identify Subgroups Among Home Health Patients With Heart Failure Using Telehealth.

Computers, informatics, nursing : CIN
This study explored the use of unsupervised machine learning to identify subgroups of patients with heart failure who used telehealth services in the home health setting, and examined intercluster differences for patient characteristics related to me...

Machine Learning Analysis of Left Ventricular Function to Characterize Heart Failure With Preserved Ejection Fraction.

Circulation. Cardiovascular imaging
BACKGROUND: Current diagnosis of heart failure with preserved ejection fraction (HFpEF) is suboptimal. We tested the hypothesis that comprehensive machine learning (ML) of left ventricular function at rest and exercise objectively captures difference...

Machine learning in heart failure: ready for prime time.

Current opinion in cardiology
PURPOSE OF REVIEW: The aim of this review is to present an up-to-date overview of the application of machine learning methods in heart failure including diagnosis, classification, readmissions and medication adherence.

Big-Data Analysis, Cluster Analysis, and Machine-Learning Approaches.

Advances in experimental medicine and biology
Medicine will experience many changes in the coming years because the so-called "medicine of the future" will be increasingly proactive, featuring four basic elements: predictive, personalized, preventive, and participatory. Drivers for these changes...

Predicting Risk of 30-Day Readmissions Using Two Emerging Machine Learning Methods.

Studies in health technology and informatics
Decades-long research efforts have shown that Heart Failure (HF) is the most expensive diagnosis for hospitalizations and the most frequent diagnosis for 30-day readmissions. If risk stratification for readmission of HF patients could be carried out ...

Novel Wearable Seismocardiography and Machine Learning Algorithms Can Assess Clinical Status of Heart Failure Patients.

Circulation. Heart failure
BACKGROUND: Remote monitoring of patients with heart failure (HF) using wearable devices can allow patient-specific adjustments to treatments and thereby potentially reduce hospitalizations. We aimed to assess HF state using wearable measurements of ...

Machine Learning Algorithm Predicts Cardiac Resynchronization Therapy Outcomes: Lessons From the COMPANION Trial.

Circulation. Arrhythmia and electrophysiology
BACKGROUND: Cardiac resynchronization therapy (CRT) reduces morbidity and mortality in heart failure patients with reduced left ventricular function and intraventricular conduction delay. However, individual outcomes vary significantly. This study so...

NT-proBNP test with improved accuracy for the diagnosis of chronic heart failure.

Medicine
The circulating concentration of N-terminal pro-brain natriuretic peptide (NT-proBNP) has been shown to be a diagnostic tool for the detection of heart failure. Several factors influence NT-proBNP levels including age, sex, and body mass index (BMI)....