AIMC Topic: Heart Failure

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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)....

Congestive heart failure information extraction framework for automated treatment performance measures assessment.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: This paper describes a new congestive heart failure (CHF) treatment performance measure information extraction system - CHIEF - developed as part of the Automated Data Acquisition for Heart Failure project, a Veterans Health Administration...

Individualized Knowledge Graph: A Viable Informatics Path to Precision Medicine.

Circulation research
We present here a vision of individualized Knowledge Graphs (iKGs) in cardiovascular medicine: a modern informatics platform of exchange and inquiry that comprehensively integrates biological knowledge with medical histories and health outcomes of in...