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<p>Percutaneous coronary intervention (PCI) is a cornerstone treatment for coronary artery disease, yet accurate prediction of long-term mortality remains a critical challenge due to the complex interplay of risk factors. Existing prognostic models rely predominantly on structured clinical data, overlooking the rich, nuanced information embedded in diagnostic imaging and procedural narratives. To address this gap, we present a novel multimodal machine learning framework that integrates coronary angiography video, unstructured procedural text, and structured clinical variables to predict 5-year all-cause mortality. Utilizing a large real-world cohort of 10,353 patients, we extracted visual embeddings via CLIP, textual embeddings via BioBERT, and structured features to construct a unified patient representation. Our trimodal LightGBM model achieved an AUC-ROC of 0.814, significantly outperforming single- and dual-modality baselines (<InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(p&lt;0.01\)</EquationSource> </InlineEquation>). SHAP-based analysis revealed that unstructured data captured complementary prognostic signals, while structured variables provided concentrated predictive strength. This study demonstrates the prognostic value of integrating heterogeneous data sources and establishes a robust, explainable foundation for precision medicine in interventional cardiology.</p>

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Multimodal machine learning for 5-year mortality prediction after percutaneous coronary intervention

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月23日

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  • Byeolhee Kim,
  • Jungyo Suh,
  • Young-Hak Kim,
  • Jung-Min Ahn,
  • Tae Joon Jun

摘要

Percutaneous coronary intervention (PCI) is a cornerstone treatment for coronary artery disease, yet accurate prediction of long-term mortality remains a critical challenge due to the complex interplay of risk factors. Existing prognostic models rely predominantly on structured clinical data, overlooking the rich, nuanced information embedded in diagnostic imaging and procedural narratives. To address this gap, we present a novel multimodal machine learning framework that integrates coronary angiography video, unstructured procedural text, and structured clinical variables to predict 5-year all-cause mortality. Utilizing a large real-world cohort of 10,353 patients, we extracted visual embeddings via CLIP, textual embeddings via BioBERT, and structured features to construct a unified patient representation. Our trimodal LightGBM model achieved an AUC-ROC of 0.814, significantly outperforming single- and dual-modality baselines ( \(p<0.01\) ). SHAP-based analysis revealed that unstructured data captured complementary prognostic signals, while structured variables provided concentrated predictive strength. This study demonstrates the prognostic value of integrating heterogeneous data sources and establishes a robust, explainable foundation for precision medicine in interventional cardiology.