ArticleInternational journal of epidemiology2022
Predicting cardiovascular risk from national administrative databases using a combined survival analysis and deep learning approach.
Article in International journal of epidemiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 4 of them syntheses that pooled it.
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Who cites it
29 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- A Systematic Review of Artificial Intelligence Models for Time-to-Event Outcome Applied in Cardiovascular Disease Risk Prediction.Journal of medical systems · 2024Pooled it
- Comprehensive Factors for Predicting the Complications of DiabetesMellitus: A Systematic Review.Current diabetes reviews · 2024Pooled it
- Application of machine learning in measurement of ageing and geriatric diseases: a systematic review.BMC geriatrics · 2023Pooled it
- Machine-learning versus traditional approaches for atherosclerotic cardiovascular risk prognostication in primary prevention cohorts: a systematic review and meta-analysis.European heart journal. Quality of care & clinical outcomes · 2023Pooled it
- Quantifying the Predictive Power of Social Determinants of Health in Cardiovascular Disease and Type 2 Diabetes Progression Using XGBoost: Retrospective Cohort Study.JMIR medical informatics · 2026Article
- Survival After the First Myocardial Infarction in Older Women: A Prospective Cohort Analysis From the WHI.Journal of the American Heart Association · 2026Article
- A feature selection-based oblique hyperplane for oblique random survival forests.BMC medical research methodology · 2026Article
- Population-level cardiovascular risk prediction models including biochemical predictors in 800 000 individuals.European heart journal open · 2026Article
- Development and internal validation of a machine learning-based prediction model for pulmonary hypertension in COPD.Frontiers in medicine · 2026Article
- Deep learning for cardiovascular disease: a comprehensive review of detection and risk forecasting.Frontiers in artificial intelligence · 2026Review
- Redefining cardiometabolic biomarkers in the big data era: toward a personalized medicine-centered reconstruction of risk prediction models.Frontiers in cardiovascular medicine · 2026Article
- Machine learning based model for predicting cardiovascular disease using dynamic triglyceride-glucose index: a longitudinal study cohort CHARLS database.Journal of geriatric cardiology : JGC · 2025Article
- Global burden of disease analysis of stroke due to high systolic blood pressure in different regions, 1990-2021.BMC public health · 2025Article
- Prognostic stratification of familial hypercholesterolaemia patients using AI algorithms: a gender-specific approach.European heart journal. Digital health · 2025Article
- Predicting cardiovascular risk with hybrid ensemble learning and explainable AI.Scientific reports · 2025Article
- Revolutionizing Utility of Big Data Analytics in Personalized Cardiovascular Healthcare.Bioengineering (Basel, Switzerland) · 2025Review
- Cardiovascular Risk Estimation in Colombia Using Artificial Intelligence Techniques.Cardiology research and practice · 2025Article
- Machine learning based prediction models for cardiovascular disease risk using electronic health records data: systematic review and meta-analysis.European heart journal. Digital health · 2025Review
- Bias in machine learning applications to address non-communicable diseases at a population-level: a scoping review.BMC public health · 2024Article
- Pitfalls in Developing Machine Learning Models for Predicting Cardiovascular Diseases: Challenge and Solutions.Journal of medical Internet research · 2024Article
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7 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundMachine 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 learning extensions of survival analysis models with Cox proportional hazards models for predicting cardiovascular disease (CVD) risk in national health administrative datasets.
methodsUsing individual person linkage of administrative datasets, we constructed a cohort of all New Zealanders aged 30-74 who interacted with public health services during 2012. After excluding people with prior CVD, we developed sex-specific deep learning and Cox proportional hazards models to estimate the risk of CVD events within 5 years. Models were compared based on the proportion of explained variance, model calibration and discrimination, and hazard ratios for predictor variables.
resultsFirst CVD events occurred in 61 927 of 2 164 872 people. Within the reference group, the largest hazard ratios estimated by the deep learning models were for tobacco use in women (2.04, 95% CI: 1.99, 2.10) and chronic obstructive pulmonary disease with acute lower respiratory infection in men (1.56, 95% CI: 1.50, 1.62). Other identified predictors (e.g. hypertension, chest pain, diabetes) aligned with current knowledge about CVD risk factors. Deep learning outperformed Cox proportional hazards models on the basis of proportion of explained variance (R2: 0.468 vs 0.425 in women and 0.383 vs 0.348 in men), calibration and discrimination (all P <0.0001).
conclusionsDeep learning extensions of survival analysis models can be applied to large health administrative datasets to derive interpretable CVD risk prediction equations that are more accurate than traditional Cox proportional hazards models.
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