Multimodal machine learning for 5-year mortality prediction after percutaneous coronary intervention
Byeolhee Kim1,2, Jungyo Suh3, Young-Hak Kim4
1Department of Medical Science, Asan Medical Center, Asan Medical Institute of Convergence Science and Technology, University of Ulsan College of Medicine, Olympic-ro 43-gil, Songpa-gu, Seoul, 05505, Republic of Korea.
Scientific Reports
|December 22, 2025
Summary
Predicting long-term mortality after percutaneous coronary intervention (PCI) is improved by a new AI model. This multimodal approach integrates imaging, text, and clinical data for better patient outcomes in cardiology.
Area of Science:
- Cardiology
- Artificial Intelligence
- Medical Informatics
Background:
- Percutaneous coronary intervention (PCI) is a key treatment for coronary artery disease.
- Accurate long-term mortality prediction post-PCI is challenging due to complex risk factors.
- Current models often miss prognostic information in imaging and procedural narratives.
Purpose of the Study:
- To develop a multimodal machine learning framework for predicting 5-year all-cause mortality after PCI.
- To integrate coronary angiography video, procedural text, and clinical data for enhanced prediction.
- To establish a foundation for precision medicine in interventional cardiology.
Main Methods:
- A cohort of 10,353 patients undergoing PCI was analyzed.
- Visual embeddings (CLIP) and textual embeddings (BioBERT) were extracted from medical data.
- A trimodal LightGBM model combined visual, textual, and structured clinical features.
Main Results:
- The trimodal model achieved an AUC-ROC of 0.814 for 5-year mortality prediction.
- This significantly outperformed single- and dual-modality models.
- SHAP analysis confirmed complementary prognostic value from unstructured data.
Conclusions:
- Integrating diverse data sources (imaging, text, clinical) significantly improves mortality prediction after PCI.
- Multimodal AI offers a robust and explainable approach for personalized cardiology.
- This framework advances precision medicine by leveraging comprehensive patient information.
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