AIMC Topic: Heart Failure

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Classification of normal sinus rhythm, abnormal arrhythmia and congestive heart failure ECG signals using LSTM and hybrid CNN-SVM deep neural networks.

Computer methods in biomechanics and biomedical engineering
Effective monitoring of heart patients according to heart signals can save a huge amount of life. In the last decade, the classification and prediction of heart diseases according to ECG signals has gained great importance for patients and doctors. I...

The role of sacubitril/valsartan in the treatment of chronic heart failure with reduced ejection fraction in hypertensive patients with comorbidities: From clinical trials to real-world settings.

Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie
BACKGROUND: Sacubitril/valsartan, the first agent to be approved in a new class of drugs called angiotensin receptor neprilysin inhibitors (ARNIs), has been shown to reduce cardiovascular mortality and morbidity compared to enalapril in outpatient su...

Machine learning-based classification and diagnosis of clinical cardiomyopathies.

Physiological genomics
Dilated cardiomyopathy (DCM) and ischemic cardiomyopathy (ICM) are two common types of cardiomyopathies leading to heart failure. Accurate diagnostic classification of different types of cardiomyopathies is critical for precision medicine in clinical...

Diagnosing Heart Failure from Chest X-Ray Images Using Deep Learning.

International heart journal
The development of deep learning technology has enabled machines to achieve high-level accuracy in interpreting medical images. While many previous studies have examined the detection of pulmonary nodules in chest X-rays using deep learning, the appl...

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...