AIMC Topic: Heart Failure

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Evidence-based Management of Heart Failure in the Systemic Right Ventricle.

Current cardiology reports
PURPOSE OF REVIEW: Explore the clinical progression, diagnostic challenges, and evolving treatments of systemic right ventricular (SRV) failure, highlighting key gaps and advances.

Deep Learning Predicts Cardiac Output from Seismocardiographic Signals in Heart Failure.

The American journal of cardiology
Determination of cardiac output (CO) is essential to the clinical management of cardiovascular compromise. However, the invasiveness, procedural risks, and reliance on specialized infrastructure limit accessibility and scalability of standard-of-care...

Development of a deep learning model for survival prediction in heart failure: competing risk and frailty model.

Scientific reports
This study presents a novel deep learning (DL) framework, the Deep Neural Frailty Competing Risks (DNFCR) model, which simultaneously integrates frailty and competing risks (CR) for mortality prediction in heart failure (HF). While existing models li...

The Impact of Comorbidity Patterns on Clinical Outcomes in Heart Failure: A Machine Learning-Based Cluster Analysis.

The American journal of cardiology
Heart failure (HF) is a major global health burden, and complex comorbidity patterns can worsen clinical outcomes and complicate patient care. This study aimed to identify distinct comorbidity-based clusters among HF patients and evaluate their assoc...

Predicting the future risk and outcomes of severe heart failure and coronary artery disease with machine learning in the UK Biobank Cohort.

PloS one
BACKGROUND: In order to seriously impact the global burden of heart failure (HF) and coronary artery disease (CAD), identifying at-risk individuals as early as possible is vital. Risk calculator tools in wide clinical use today are informed by tradit...

VO Max in Clinical Cardiology: Clinical Applications, Evidence Gaps, and Future Directions.

Current cardiology reports
PURPOSE OF REVIEW: VO₂ max is a fundamental marker of cardiorespiratory fitness with substantial prognostic and diagnostic value within the field of cardiology. This review analyzes current and emerging evidence regarding its clinical uses, highlight...

Explainable mortality prediction models incorporating social health determinants and physical frailty for heart failure patients.

PloS one
There is limited evidence on how social determinants of health (SDOH) and physical frailty (PF) influence mortality prediction in heart failure (HF), particularly for in-hospital, 90-day, and 1-year outcomes. This study aims to develop explainable ma...

Uncovering key biomarkers, potential therapeutic targets and development of deep learning model in heart failure.

PloS one
Heart failure (HF) represents a significant public health concern, characterized by elevated rates of mortality and morbidity. Recent advancements in gene sequencing technologies have led to the identification of numerous genes associated with heart ...

A heart failure classification model from radial artery pulse wave using LSTM neural networks.

BMC medical informatics and decision making
BACKGROUND: Heart failure (HF) represents a pressing global health issue demanding innovative and accessible approaches for early detection. Non-invasive, rapid, and cost-effective techniques utilizing deep learning (DL) hold significant promise for ...

Machine learning enhanced expert system for detecting heart failure decompensation using patient reported vitals and electronic health records.

Scientific reports
Heart failure (HF) is a condition with periods of stability interrupted by periods of worsening symptoms, known as decompensation episodes. Digital interventions are promising tools to alleviate burdens on HF management through automated alerts at th...