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Heart Failure

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Artificial intelligence universal biomarker prediction tool.

Journal of thrombosis and thrombolysis
Through experiencing cardiopulmonary arrest, an artificial intelligence universal biomarker prediction tool was developed to help patients understand improvement in the trends of their disease. PyPI tool handles two biomarkers, hbA1c for diabetes and...

The PACIFIC ontology for heterogeneous data management in cardiology.

Journal of biomedical informatics
With the emergence of health data warehouses and major initiatives to collect and analyze multi-modal and multisource data, data organization becomes central. In the PACIFIC-PRESERVED (PhenomApping, ClassIFication, and Innovation for Cardiac Dysfunct...

BOO-ST and CBCEC: two novel hybrid machine learning methods aim to reduce the mortality of heart failure patients.

Scientific reports
Heart failure (HF) is a leading cause of mortality worldwide. Machine learning (ML) approaches have shown potential as an early detection tool for improving patient outcomes. Enhancing the effectiveness and clinical applicability of the ML model nece...

EstimATTR: A Simplified, Machine-Learning-Based Tool to Predict the Risk of Wild-Type Transthyretin Amyloid Cardiomyopathy.

Journal of cardiac failure
BACKGROUND: Wild-type transthyretin amyloid cardiomyopathy (ATTRwt-CM), an increasingly recognized cause of heart failure (HF), often remains undiagnosed until later stages of the disease.

Deep learning-based NT-proBNP prediction from the ECG for risk assessment in the community.

Clinical chemistry and laboratory medicine
OBJECTIVES: The biomarker N-terminal pro B-type natriuretic peptide (NT-proBNP) has predictive value for identifying individuals at risk for cardiovascular disease (CVD). However, it is not widely used for screening in the general population, potenti...

Forecasting the Acute Heart Failure Admissions: Development of Deep Learning Prediction Model Incorporating the Climate Information.

Journal of cardiac failure
BACKGROUND: Climate is known to influence the incidence of cardiovascular events. However, their prediction with traditional statistical models remains imprecise.

MultiCOVID: a multi modal deep learning approach for COVID-19 diagnosis.

Scientific reports
The rapid spread of the severe acute respiratory syndrome coronavirus 2 led to a global overextension of healthcare. Both Chest X-rays (CXR) and blood test have been demonstrated to have predictive value on Coronavirus Disease 2019 (COVID-19) diagnos...

Vascular Age Assessed From an Uncalibrated, Noninvasive Pressure Waveform by Using a Deep Learning Approach: The AI-VascularAge Model.

Hypertension (Dallas, Tex. : 1979)
BACKGROUND: Aortic stiffness, assessed as carotid-femoral pulse wave velocity, provides a measure of vascular age and risk for adverse cardiovascular disease outcomes, but it is difficult to measure. The shape of arterial pressure waveforms conveys i...

Artificial intelligence models in prediction of response to cardiac resynchronization therapy: a systematic review.

Heart failure reviews
The aim of the presented review is to summarize the literature data on the accuracy and clinical applicability of artificial intelligence (AI) models as a valuable alternative to the current guidelines in predicting cardiac resynchronization therapy ...