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Heart Failure

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Clinical applications of machine learning in the diagnosis, classification, and prediction of heart failure.

American heart journal
Machine learning and artificial intelligence are generating significant attention in the scientific community and media. Such algorithms have great potential in medicine for personalizing and improving patient care, including in the diagnosis and man...

Identification of Patients with Heart Failure in Large Datasets.

Heart failure clinics
Large registries, administrative data, and the electronic health record (EHR) offer opportunities to identify patients with heart failure, which can be used for research purposes, process improvement, and optimal care delivery. Identification of case...

Interpretable clinical prediction via attention-based neural network.

BMC medical informatics and decision making
BACKGROUND: The interpretability of results predicted by the machine learning models is vital, especially in the critical fields like healthcare. With the increasingly adoption of electronic healthcare records (EHR) by the medical organizations in th...

Predicting High-Risk Patients and High-Risk Outcomes in Heart Failure.

Heart failure clinics
Identifying patients with heart failure at high risk for poor outcomes is important for patient care, resource allocation, and process improvement. Although numerous risk models exist to predict mortality, hospitalization, and patient-reported health...

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...