AIMC Topic: Heart Disease Risk Factors
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A validated multivariable machine learning model to predict cardio-kidney risk in diabetic kidney disease.
Cardiovascular diabetology
May 15, 2025
BACKGROUND: Individuals with diabetic kidney disease (DKD) often suffer cardiac and kidney events. We sought to develop an accurate means by which to stratify risk in DKD.
Female
Reproducibility of Results
Risk Factors
Aged
Male
Machine Learning
Decision Support Techniques
Biomarkers
Humans
Prognosis
Diabetic Nephropathies
Predictive Value of Tests
Cardiovascular Diseases
Heart Diseases
Middle Aged
Heart Disease Risk Factors
Risk Assessment
Sodium-Glucose Transporter 2 Inhibitors
Time Factors
Machine Learning Model for Predicting Coronary Heart Disease Risk: Development and Validation Using Insights From a Japanese Population-Based Study.
JMIR cardio
May 12, 2025
BACKGROUND: Coronary heart disease (CHD) is a major cause of morbidity and mortality worldwide. Identifying key risk factors is essential for effective risk assessment and prevention. A data-driven approach using machine learning (ML) offers advanced...
Artificial intelligence-enhanced electrocardiography for the identification of a sex-related cardiovascular risk continuum: a retrospective cohort study.
The Lancet. Digital health
Mar 1, 2025
BACKGROUND: Females are typically underserved in cardiovascular medicine. The use of sex as a dichotomous variable for risk stratification fails to capture the heterogeneity of risk within each sex. We aimed to develop an artificial intelligence-enha...
Machine Learning-Assisted Analysis of Sublingual Microcirculatory Dysfunction for Early Cardiovascular Risk Evaluation and Cardiovascular-Kidney-Metabolic Syndrome Stage in Patients With Type 2 Diabetes Mellitus.
Diabetes/metabolism research and reviews
Sep 1, 2024
AIMS: To examine whether sublingual microcirculation can be used as an effective and noninvasive method for assessing cardiovascular, kidney, and metabolic risks in patients with type 2 diabetes mellitus (T2DM).
The Diagnosis of Cardiovascular Disease Using Simple Blood Biomarkers Through AI and Big Data.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Jul 1, 2024
Cardiovascular disease (CVD) is the leading cause of global mortality, diagnosed primarily through costly imaging modalities which are often overused in asymptomatic patients. Our project aims to develop an AI-based solution for CVD risk stratificati...
Predicting cardiovascular risk from national administrative databases using a combined survival analysis and deep learning approach.
International journal of epidemiology
Jun 13, 2022
BACKGROUND: Machine learning-based risk prediction models may outperform traditional statistical models in large datasets with many variables, by identifying both novel predictors and the complex interactions between them. This study compared deep le...
Neural network-based integration of polygenic and clinical information: development and validation of a prediction model for 10-year risk of major adverse cardiac events in the UK Biobank cohort.
The Lancet. Digital health
Feb 1, 2022
BACKGROUND: In primary cardiovascular disease prevention, early identification of high-risk individuals is crucial. Genetic information allows for the stratification of genetic predispositions and lifetime risk of cardiovascular disease. However, tow...
Identification of cardiovascular high-risk groups from dynamic retinal vessel signals using untargeted machine learning.
Cardiovascular research
Jan 29, 2022
AIMS: Dynamic retinal vessel analysis (DVA) provides a non-invasive way to assess microvascular function in patients and potentially to improve predictions of individual cardiovascular (CV) risk. The aim of our study was to use untargeted machine lea...
Cardiovascular Diseases
Middle Aged
Risk Assessment
Renal Dialysis
Treatment Outcome
Cluster Analysis
Predictive Value of Tests
Kidney Failure, Chronic
Cause of Death
Machine Learning
Female
Aged
Male
Humans
Heart Disease Risk Factors
Signal Processing, Computer-Assisted
Photic Stimulation
Workflow
Retinal Vessels
Light
Arterioles
Vasodilation
Venules
Joint Associations of Multiple Dietary Components With Cardiovascular Disease Risk: A Machine-Learning Approach.
American journal of epidemiology
Jul 1, 2021
The human diet consists of a complex mixture of components. To realistically assess dietary impacts on health, new statistical tools that can better address nonlinear, collinear, and interactive relationships are necessary. Using data from 1,928 heal...
Artificial Intelligence and Hypertension: Recent Advances and Future Outlook.
American journal of hypertension
Nov 3, 2020
Prevention and treatment of hypertension (HTN) are a challenging public health problem. Recent evidence suggests that artificial intelligence (AI) has potential to be a promising tool for reducing the global burden of HTN, and furthering precision me...
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