ArticleJournal of clinical medicine2022
Deep Learning Improves Prediction of Cardiovascular Disease-Related Mortality and Admission in Patients with Hypertension: Analysis of the Korean National Health Information Database.
Article in Journal of clinical medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Optimization of artificial intelligence models for prediction of new-onset cardiovascular disease in patients with arterial hypertension.PLOS digital health · 2026Article
- Deep learning-based prognosis of major adverse cardiac events in patients with acute myocardial infarction: a retrospective observational study in the Republic of Korea.Osong public health and research perspectives · 2025Article
- Data-driven prediction of cardiovascular and cerebrovascular diseases in a nationwide study.Scientific reports · 2025Article
- A Multitask Deep Learning Model for Predicting Myocardial Infarction Complications.Bioengineering (Basel, Switzerland) · 2025Article
- Artificial intelligence for cardiovascular disease risk assessment in personalised framework: a scoping review.EClinicalMedicine · 2024Article
- Application of artificial intelligence in hypertension.Clinical hypertension · 2024Review
- Artificial Intelligence in Cardiology: Why So Many Great Promises and Expectations, but Still a Limited Clinical Impact?Journal of clinical medicine · 2023Article
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Abstract
objectiveThe aim of this study was to develop, compare, and validate models for predicting cardiovascular disease (CVD) mortality and hospitalization with hypertension using a conventional statistical model and a deep learning model.
methodsUsing the database of Korean National Health Insurance Service, 2,037,027 participants with hypertension were identified. Among them, CVD (myocardial infarction or stroke) death and/or hospitalization that occurred within one year after the last visit were analyzed. Oversampling was performed using the synthetic minority oversampling algorithm to resolve imbalances in the number of samples between case and control groups. The logistic regression method and deep neural network (DNN) method were used to train models for assessing the risk of mortality and hospitalization.
findingsDeep learning-based prediction model showed a higher performance in all datasets than the logistic regression model in predicting CVD hospitalization (accuracy, 0.863 vs. 0.655; F
interpretationThe deep learning model could accurately predict CVD hospitalization and death within a year in patients with hypertension. The findings of this study could allow for prevention and monitoring by allocating resources to high-risk patients.
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