AIMC Topic: Heart Failure
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Artificial intelligence-assisted automated heart failure detection and classification from electronic health records.
ESC heart failure
May 3, 2024
AIMS: Electronic health records (EHR) linked to Digital Imaging and Communications in Medicine (DICOM), biological specimens, and deep learning (DL) algorithms could potentially improve patient care through automated case detection and surveillance. ...
Improving the Prognostic Evaluation Precision of Hospital Outcomes for Heart Failure Using Admission Notes and Clinical Tabular Data: Multimodal Deep Learning Model.
Journal of medical Internet research
May 2, 2024
BACKGROUND: Clinical notes contain contextualized information beyond structured data related to patients' past and current health status.
Artificial Intelligence Predicts Hospitalization for Acute Heart Failure Exacerbation in Patients Undergoing Myocardial Perfusion Imaging.
Journal of nuclear medicine : official publication, Society of Nuclear Medicine
May 1, 2024
Heart failure (HF) is a leading cause of morbidity and mortality in the United States and worldwide, with a high associated economic burden. This study aimed to assess whether artificial intelligence models incorporating clinical, stress test, and im...
Accuracy and consistency of online large language model-based artificial intelligence chat platforms in answering patients' questions about heart failure.
International journal of cardiology
Apr 30, 2024
BACKGROUND: Heart failure (HF) is a prevalent condition associated with significant morbidity. Patients may have questions that they feel embarrassed to ask or will face delays awaiting responses from their healthcare providers which may impact their...
Identification of common mechanisms and biomarkers of atrial fibrillation and heart failure based on machine learning.
ESC heart failure
Apr 24, 2024
AIMS: Atrial fibrillation (AF) is the most common arrhythmia. Heart failure (HF) is a disease caused by heart dysfunction. The prevalence of AF and HF were progressively increasing over time. The co-existence of AF and HF presents a significant thera...
AI-enabled cardiac chambers volumetry in coronary artery calcium scans (AI-CAC) predicts heart failure and outperforms NT-proBNP: The multi-ethnic study of Atherosclerosis.
Journal of cardiovascular computed tomography
Apr 24, 2024
INTRODUCTION: Coronary artery calcium (CAC) scans contain useful information beyond the Agatston CAC score that is not currently reported. We recently reported that artificial intelligence (AI)-enabled cardiac chambers volumetry in CAC scans (AI-CAC™...
Asymptomatic Diseases
Computed Tomography Angiography
Natriuretic Peptide, Brain
Multidetector Computed Tomography
Vascular Calcification
Male
Middle Aged
Peptide Fragments
Humans
Risk Assessment
Time Factors
Radiographic Image Interpretation, Computer-Assisted
Artificial Intelligence
Incidence
United States
Coronary Artery Disease
Biomarkers
Reproducibility of Results
Heart Failure
Predictive Value of Tests
Prognosis
Risk Factors
Female
Aged
Coronary Angiography
Aged, 80 and over
The Efficacy of Machine Learning Models for Predicting the Prognosis of Heart Failure: A Systematic Review and Meta-Analysis.
Cardiology
Apr 22, 2024
INTRODUCTION: Heart failure (HF) is a major global public health concern. The application of machine learning (ML) to identify individuals at high risk and enable early intervention is a promising approach for improving HF prognosis. We aim to system...
Predicting in-hospital mortality among patients admitted with a diagnosis of heart failure: a machine learning approach.
ESC heart failure
Apr 18, 2024
Existing risk prediction models for hospitalized heart failure patients are limited. We identified patients hospitalized with a diagnosis of heart failure between 7 May 2013 and 26 April 2022 from a large academic, quaternary care medical centre (tra...
Identification and validation of aging-related genes in heart failure based on multiple machine learning algorithms.
Frontiers in immunology
Apr 15, 2024
BACKGROUND: In the face of continued growth in the elderly population, the need to understand and combat age-related cardiac decline becomes even more urgent, requiring us to uncover new pathological and cardioprotective pathways.
Prediction of adverse cardiovascular events in children using artificial intelligence-based electrocardiogram.
International journal of cardiology
Apr 3, 2024
BACKGROUND: Convolutional neural networks (CNNs) have emerged as a novel method for evaluating heart failure (HF) in adult electrocardiograms (ECGs). However, such CNNs are not applicable to pediatric HF, where abnormal anatomy of congenital heart de...
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