AIMC Topic: Heart Failure

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Performance of risk models to predict mortality risk for patients with heart failure: evaluation in an integrated health system.

Clinical research in cardiology : official journal of the German Cardiac Society
BACKGROUND: Referral of patients with heart failure (HF) who are at high mortality risk for specialist evaluation is recommended. Yet, most tools for identifying such patients are difficult to implement in electronic health record (EHR) systems.

Individualized Treatment Effect Prediction with Machine Learning - Salient Considerations.

NEJM evidence
BACKGROUND: Machine learning-based approaches that seek to accomplish individualized treatment effect prediction have gained traction; however, some salient challenges lack wider recognition.

Prediction of cardiovascular and renal risk among patients with apparent treatment-resistant hypertension in the United States using machine learning methods.

Journal of clinical hypertension (Greenwich, Conn.)
Apparent treatment-resistant hypertension (aTRH), defined as blood pressure (BP) that remains uncontrolled despite unconfirmed concurrent treatment with three antihypertensives, is associated with an increased risk of developing cardiovascular and re...

Circadian assessment of heart failure using explainable deep learning and novel multi-parameter polar images.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Heart failure (HF) is a multi-faceted and life-threatening syndrome that affects more than 64.3 million people worldwide. Current gold-standard screening technique, echocardiography, neglects cardiovascular information regul...

Development and validation of an electrocardiographic artificial intelligence model for detection of peripartum cardiomyopathy.

American journal of obstetrics & gynecology MFM
BACKGROUND: This study used electrocardiogram data in conjunction with artificial intelligence methods as a noninvasive tool for detecting peripartum cardiomyopathy.

hART: Deep learning-informed lifespan heart failure risk trajectories.

International journal of medical informatics
BACKGROUND: Heart failure (HF) results in persistent risk and long-term comorbidities. This is particularly true for patients with lifelong HF sequelae of cardiovascular disease such as patients with congenital heart disease (CHD).

Examining arterial pulsation to identify and risk-stratify heart failure subjects with deep neural network.

Physical and engineering sciences in medicine
Hemodynamic parameters derived from pulse wave analysis have been shown to predict long-term outcomes in patients with heart failure (HF). Here we aimed to develop a deep-learning based algorithm that incorporates pressure waveforms for the identific...

Enhancing heart failure treatment decisions: interpretable machine learning models for advanced therapy eligibility prediction using EHR data.

BMC medical informatics and decision making
Timely and accurate referral of end-stage heart failure patients for advanced therapies, including heart transplants and mechanical circulatory support, plays an important role in improving patient outcomes and saving costs. However, the decision-mak...

Artificial Intelligence in Heart Failure and Acute Kidney Injury: Emerging Concepts and Controversial Dimensions.

Cardiorenal medicine
BACKGROUND: The growing complexity of patient data and the intricate relationship between heart failure (HF) and acute kidney injury (AKI) underscore the potential benefits of integrating artificial intelligence (AI) and machine learning into healthc...