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Heart Failure

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Predictive performance of machine and statistical learning methods: Impact of data-generating processes on external validity in the "large N, small p" setting.

Statistical methods in medical research
Machine learning approaches are increasingly suggested as tools to improve prediction of clinical outcomes. We aimed to identify when machine learning methods perform better than a classical learning method. We hereto examined the impact of the data-...

Machine learning, artificial intelligence and mechanical circulatory support: A primer for clinicians.

The Journal of heart and lung transplantation : the official publication of the International Society for Heart Transplantation
Artificial intelligence (AI) refers to the ability of machines to perform intelligent tasks, and machine learning (ML) is a subset of AI describing the ability of machines to learn independently and make accurate predictions. The application of AI co...

Association Between Coffee Intake and Incident Heart Failure Risk: A Machine Learning Analysis of the FHS, the ARIC Study, and the CHS.

Circulation. Heart failure
BACKGROUND: Coronary heart disease, heart failure (HF), and stroke are complex diseases with multiple phenotypes. While many risk factors for these diseases are well known, investigation of as-yet unidentified risk factors may improve risk assessment...

Deep-learning-assisted analysis of echocardiographic videos improves predictions of all-cause mortality.

Nature biomedical engineering
Machine learning promises to assist physicians with predictions of mortality and of other future clinical events by learning complex patterns from historical data, such as longitudinal electronic health records. Here we show that a convolutional neur...

Recurrent disease progression networks for modelling risk trajectory of heart failure.

PloS one
MOTIVATION: Recurrent neural networks (RNN) are powerful frameworks to model medical time series records. Recent studies showed improved accuracy of predicting future medical events (e.g., readmission, mortality) by leveraging large amount of high-di...

Construction and analysis of a joint diagnosis model of random forest and artificial neural network for heart failure.

Aging
Heart failure is a global health problem that affects approximately 26 million people worldwide. As conventional diagnostic techniques for heart failure have been in practice with various limitations, it is necessary to develop novel diagnostic model...

Use of Machine Learning to Develop a Risk-Stratification Tool for Emergency Department Patients With Acute Heart Failure.

Annals of emergency medicine
STUDY OBJECTIVE: We use variables from a recently derived acute heart failure risk-stratification rule (STRATIFY) as a basis to develop and optimize risk prediction using additional patient clinical data from electronic health records and machine-lea...

Echocardiography in patients with heart failure: recent advances and future perspectives.

Kardiologia polska
Echocardiography is a relatively inexpensive and widely available technique that has a pivotal role in the assessment and management of patients with heart failure (HF). Advancements in cardiac ultrasound, especially the advent of myocardial deformat...