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<p>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.</p>

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AI approaches for predicting progression to acute coronary syndrome among stable coronary heart disease patients

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月2日

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  • Haozhong Ma,
  • Hexiang Bai,
  • Jiahuan Yan,
  • Qiyuan Chen,
  • Zihan Ma,
  • Shuo Tong,
  • Yuxuan Zhan,
  • Ruijia Wu,
  • Hongxia Xu,
  • Jian Wu

摘要

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.