AIMC Topic: Cardiovascular Diseases
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Opportunistic AI for enhanced cardiovascular disease risk stratification using abdominal CT scans.
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Jan 20, 2025
This study introduces the Deep Learning-based Cardiovascular Disease Incident (DL-CVDi) score, a novel biomarker derived from routine abdominal CT scans, optimized to predict cardiovascular disease (CVD) risk using deep survival learning. CT imaging,...
Early Prediction of Cardio Vascular Disease (CVD) from Diabetic Retinopathy using improvised deep Belief Network (I-DBN) with Optimum feature selection technique.
BMC cardiovascular disorders
Jan 18, 2025
Cardio Vascular Disease (CVD) is one of the leading causes of mortality and it is estimated that 1 in 4 deaths happens due to it. The disease prevalence rate becomes higher since there is an inadequate system/model for predicting CVD at an earliest. ...
Relationship between lifestyle factors and cardiovascular disease prevalence in Somaliland: A supervised machine learning approach using data from Hargeisa Group Hospital, 2024.
Current problems in cardiology
Jan 17, 2025
BACKGROUND: Cardiovascular diseases (CVDs) are leading contributors to global morbidity and mortality, with low- and middle-income countries experiencing disproportionately high burdens. In Somaliland, urbanization and lifestyle transitions have incr...
Comparison of cardiovascular risk prediction models developed using machine learning based on data from a Sri Lankan cohort with World Health Organization risk charts for predicting cardiovascular risk among Sri Lankans: a cohort study.
BMJ open
Jan 15, 2025
INTRODUCTION: Models derived from non-Sri Lankan cohorts are used for cardiovascular (CV) risk stratification of Sri Lankans.
Risk prediction model of cognitive performance in older people with cardiovascular diseases: a study of the National Health and Nutrition Examination Survey database.
Frontiers in public health
Jan 13, 2025
BACKGROUND AND AIM: Changes in cognitive function are commonly associated with aging in patients with cardiovascular diseases. The objective of this research was to construct and validate a nomogram-based predictive model for the identification of co...
Integrating genetics, metabolites, and clinical characteristics in predicting cardiometabolic health outcomes using machine learning algorithms - A systematic review.
Computers in biology and medicine
Jan 11, 2025
BACKGROUND: Machine learning (ML) integration of clinical, metabolite, and genetic data reveals variable results in predicting cardiometabolic health (CMH) outcomes. Therefore, we aim to (1) evaluate whether a multi-modal approach incorporating all t...
TinyML and edge intelligence applications in cardiovascular disease: A survey.
Computers in biology and medicine
Jan 10, 2025
Tiny machine learning (TinyML) and edge intelligence have emerged as pivotal paradigms for enabling machine learning on resource-constrained devices situated at the extreme edge of networks. In this paper, we explore the transformative potential of T...
Harnessing NLP to investigate biomarker interactions and CVD risks in elderly chronic kidney disease patients.
SLAS technology
Jan 9, 2025
Chronic kidney disease (CKD) significantly increases the risk of CVD diseases, particularly among elderly patients. Understanding the interaction between several biomarkers and cardiovascular (CVD) risks is crucial for improving patient outcomes and ...
Predicting cardiovascular disease in patients with mental illness using machine learning.
European psychiatry : the journal of the Association of European Psychiatrists
Jan 8, 2025
BACKGROUND: Cardiovascular disease (CVD) is twice as prevalent among individuals with mental illness compared to the general population. Prevention strategies exist but require accurate risk prediction. This study aimed to develop and validate a mach...
Autonomous International Classification of Diseases Coding Using Pretrained Language Models and Advanced Prompt Learning Techniques: Evaluation of an Automated Analysis System Using Medical Text.
JMIR medical informatics
Jan 6, 2025
BACKGROUND: Machine learning models can reduce the burden on doctors by converting medical records into International Classification of Diseases (ICD) codes in real time, thereby enhancing the efficiency of diagnosis and treatment. However, it faces ...
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