AIMC Topic: Heart Failure

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Design of a Digital Twin of the Heart for the Management of Heart Failure Patients.

Studies in health technology and informatics
Heart failure poses a significant global health burden with high prevalence and mortality rates. A promising possibility in this context is the constant monitoring of the patients through telemedicine. The aim of this work is to present a digital twi...

Explainable Machine Learning Based Prediction of Severity of Heart Failure Using Primary Electronic Health Records.

Studies in health technology and informatics
Heart Failure (HF) is a life-threatening condition. It affects more than 64 million people worldwide. Early diagnosis of HF is extremely crucial. In this study, we propose utilization of machine learning (ML) models to predict severity of HF from pri...

Stratifying heart failure patients with graph neural network and transformer using Electronic Health Records to optimize drug response prediction.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVES: Heart failure (HF) impacts millions of patients worldwide, yet the variability in treatment responses remains a major challenge for healthcare professionals. The current treatment strategies, largely derived from population based evidence...

Assessment of Serum Creatinine and Serum Sodium Prognostic Potential in Heart Failure Patients Using Machine Learning.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Heart failure (HF) is the leading etiology for hospital admissions and ranks among the foremost contributors to mortality. This complex clinical syndrome with various phenotypes is categorized by left ventricle ejection fraction levels (LVEF), namely...

Through the Looking Glass Darkly: How May AI Models Influence Future Underwriting?

Journal of insurance medicine (New York, N.Y.)
Applications of Artificial Intelligence (AI) deep-learning models to screening for clinical conditions continue to evolve. Instances provided in this treatise include using a simple one-view PA chest radiograph to screen for Type 2 Diabetes Mellitus ...

[Prediction of risk of in-hospital death in patients with chronic heart failure complicated by lung infections using interpretable machine learning].

Nan fang yi ke da xue xue bao = Journal of Southern Medical University
OBJECTIVE: To predict the risk of in-hospital death in patients with chronic heart failure (CHF) complicated by lung infections using interpretable machine learning.

Deep learning to detect left ventricular structural abnormalities in chest X-rays.

European heart journal
BACKGROUND AND AIMS: Early identification of cardiac structural abnormalities indicative of heart failure is crucial to improving patient outcomes. Chest X-rays (CXRs) are routinely conducted on a broad population of patients, presenting an opportuni...