ArticleEuropean heart journal. Digital health2024
Improving cardiovascular risk prediction through machine learning modelling of irregularly repeated electronic health records.
Article in European heart journal. Digital health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07539532 (Value of Some Risk Scores in Predicting Cardiovascular Events After Gastrointestinal Surgery), which is not on this map. Cited by 20 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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.
Value of Some Risk Scores in Predicting Cardiovascular Events After Gastrointestinal Surgery
Who cites it
20 citing papers in PubMed.
- AI-based multimodal integration of genomics and electronic health records.Nature reviews. Genetics · 2026Review
- Development and Validation of an XGBoost-SHAP Model for Predicting Adverse Outcomes in Elderly Cardiovascular Patients With Polypharmacy: A Retrospective Cohort Study.Pharmacology research & perspectives · 2026Article
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- Machine-learning-based cardiovascular mortality prediction using a cumulative PMScientific reports · 2026Observational
- Machine Learning Applications for Risk Stratification in Heart Failure with Preserved Ejection Fraction: A New Era in Cardiology.Diagnostics (Basel, Switzerland) · 2026Review
- Harnessing Clinical and Biochemical Data for Personalized Cardiovascular Risk Prediction: a Machine Learning Approach Toward Precision Nutrition.The Journal of nutrition · 2026Observational
- Sustainable and interpretable heart disease prediction: a clinical decision support approach for biomedical healthcare applications.Scientific reports · 2026Article
- Enhancing clinically cardiovascular machine learning model for risk prediction via sample augmentation.Frontiers in 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
- Understanding health-related quality of life in Chinese infertility patients: a qualitative study.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2025Article
- A Narrative Review of Multimodal Data Fusion Strategies for Precision Risk Prediction in Coronary Artery Disease: Advances, Challenges, and Future Informatics Directions.Rambam Maimonides medical journal · 2025Review
- Machine learning-based scoring system to predict cardiogenic shock in acute coronary syndrome.European heart journal. Digital health · 2025Article
- Bridging the gap between human beings and digital twins in radiology.European radiology · 2024Article
- Cardiovascular Aging and Risk Assessment: How Multimodality Imaging Can Help.Diagnostics (Basel, Switzerland) · 2024Review
- Deep learning models for predicting the survival of patients with hepatocellular carcinoma based on a surveillance, epidemiology, and end results (SEER) database analysis.Scientific reports · 2024Article
- Continuous patient state attention model for addressing irregularity in electronic health records.BMC medical informatics and decision making · 2024Article
- Using machine learning to predict acute myocardial infarction and ischemic heart disease in primary care cardiovascular patients.PloS one · 2024Article
- From data to wisdom: harnessing the power of multimodal approach for personalized atherosclerotic cardiovascular risk assessment.European heart journal. Digital health · 2024Article
- Development and Validation of a Predictive Model for Intracranial Haemorrhage in Patients on Direct Oral Anticoagulants.Clinical and applied thrombosis/hemostasis : official journal of the International Academy of Clinical and Applied Thrombosis/HemostasisArticle
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Aims: Existing electronic health records (EHRs) often consist of abundant but irregular longitudinal measurements of risk factors. In this study, we aim to leverage such data to improve the risk prediction of atherosclerotic cardiovascular disease (ASCVD) by applying machine learning (ML) algorithms, which can allow automatic screening of the population. Methods and results: A total of 215 744 Chinese adults aged between 40 and 79 without a history of cardiovascular disease were included (6081 cases) from an EHR-based longitudinal cohort study. To allow interpretability of the model, the predictors of demographic characteristics, medication treatment, and repeatedly measured records of lipids, glycaemia, obesity, blood pressure, and renal function were used. The primary outcome was ASCVD, defined as non-fatal acute myocardial infarction, coronary heart disease death, or fatal and non-fatal stroke. The eXtreme Gradient boosting (XGBoost) algorithm and Least Absolute Shrinkage and Selection Operator (LASSO) regression models were derived to predict the 5-year ASCVD risk. In the validation set, compared with the refitted Chinese guideline-recommended Cox model (i.e. the China-PAR), the XGBoost model had a significantly higher Conclusion: Machine learning algorithms with irregular, repeated real-world data could improve cardiovascular risk prediction. They demonstrated significantly better performance for reclassification to identify the high-risk population correctly.
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