ArticleKorean circulation journal2020
Development and External Validation of a Deep Learning Algorithm for Prognostication of Cardiovascular Outcomes.
Article in Korean circulation journal, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT02931500 (MachIne LEarning for Identification of Future Development of cardiovaScular and meTabOlic Disease), which is not on this map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
MachIne LEarning for Identification of Future Development of cardiovaScular and meTabOlic Disease: a NationwidE Time-series Cohort Study (MILESTONE)
Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence in the risk prediction models of cardiovascular disease and development of an independent validation screening tool: a systematic review.BMC medicine · 2024Pooled it
- The PREVENT Risk Equations and Cardiovascular Prevention in East Asia.Journal of atherosclerosis and thrombosis · 2026Review
- Atherosclerotic Cardiovascular Disease Risk Prediction Models in China, Japan, and Korea: Implications for East Asians?JACC. Asia · 2025Review
- 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
- Pitfalls in Developing Machine Learning Models for Predicting Cardiovascular Diseases: Challenge and Solutions.Journal of medical Internet research · 2024Article
- Survey and Evaluation of Hypertension Machine Learning Research.Journal of the American Heart Association · 2023Article
- Objectives, design and main findings until 2020 from the Rotterdam Study.European journal of epidemiology · 2020Article
- Machine Learning: a New Opportunity for Risk Prediction.Korean circulation journal · 2020Article
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
BACKGROUND AND
objectivesWe aim to explore the additional discriminative accuracy of a deep learning (DL) algorithm using repeated-measures data for identifying people at high risk for cardiovascular disease (CVD), compared to Cox hazard regression.
methodsTwo CVD prediction models were developed from National Health Insurance Service-Health Screening Cohort (NHIS-HEALS): a Cox regression model and a DL model. Performance of each model was assessed in the internal and 2 external validation cohorts in Koreans (National Health Insurance Service-National Sample Cohort; NHIS-NSC) and in Europeans (Rotterdam Study). A total of 412,030 adults in the NHIS-HEALS; 178,875 adults in the NHIS-NSC; and the 4,296 adults in Rotterdam Study were included.
resultsMean ages was 52 years (46% women) and there were 25,777 events (6.3%) in NHIS-HEALS during the follow-up. In internal validation, the DL approach demonstrated a C-statistic of 0.896 (95% confidence interval, 0.886-0.907) in men and 0.921 (0.908-0.934) in women and improved reclassification compared with Cox regression (net reclassification index [NRI], 24.8% in men, 29.0% in women). In external validation with NHIS-NSC, DL demonstrated a C-statistic of 0.868 (0.860-0.876) in men and 0.889 (0.876-0.898) in women, and improved reclassification compared with Cox regression (NRI, 24.9% in men, 26.2% in women). In external validation applied to the Rotterdam Study, DL demonstrated a C-statistic of 0.860 (0.824-0.897) in men and 0.867 (0.830-0.903) in women, and improved reclassification compared with Cox regression (NRI, 36.9% in men, 31.8% in women).
conclusionsA DL algorithm exhibited greater discriminative accuracy than Cox model approaches.
trial registrationClinicalTrials.gov Identifier: NCT02931500.
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