ArticleNPJ cardiovascular health2025
AI approaches for predicting progression to acute coronary syndrome among stable coronary heart disease patients.
Article in NPJ cardiovascular health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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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.
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Who cites it
2 citing papers in PubMed.
- Prediction of Risk of Cardiovascular Events in Patients With Stable Angina Using Artificial Intelligence: A Systematic Review.Cardiology research · 2026Review
- A fine-grained transformer combined with multimodal data for predicting hospital length of stay in acute coronary syndrome.Scientific reports · 2026Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
Coronary heart disease (CHD), including its acute manifestation, acute coronary syndrome (ACS), remains a leading cause of morbidity and mortality worldwide. Early identification of ACS is critical for reducing the global burden of CHD, yet traditional diagnostic methods are often invasive, costly, and time consuming. In this study, we introduce an AI-driven approach that utilizes electronic health records (EHR) to identify transitions from stable CHD to ACS, using a comprehensive dataset of 12,336 patient records from 131 medical institutions in Jiangsu Province, China. The proposed model, applying the T2G-Former to predict ACS in 12 months, demonstrated superior performance, with an area under the curve of 0.953 and a sensitivity of 0.814 on the test set. Model interpretability was supported by SHAP values, which clarified the contribution of individual clinical variables. Our findings highlight the potential of AI-enabled EHR analysis for scalable early ACS detection and clinical decision support.
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Registered trials
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