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Mortality after bleeding versus myocardial infarction in coronary artery disease: A systematic review and meta-analysis. <i>EuroIntervention<\/i> 2021; 17: 550\u2013560.","DOI":"10.4244\/EIJ-D-20-01197"},{"key":"4","doi-asserted-by":"crossref","unstructured":"4. Galli M, Gragnano F, Berteotti M, Marcucci R, Gargiulo G, Calabro P, et al. Antithrombotic therapy in high bleeding risk, Part I: Percutaneous cardiac interventions. <i>JACC Cardiovasc Interv<\/i> 2024; 17: 2197\u20132215.","DOI":"10.1016\/j.jcin.2024.08.022"},{"key":"5","doi-asserted-by":"crossref","unstructured":"5. Ueki Y, Bar S, Losdat S, Otsuka T, Zanchin C, Zanchin T, et al. Validation of the Academic Research Consortium for High Bleeding Risk (ARC-HBR) criteria in patients undergoing percutaneous coronary intervention and comparison with contemporary bleeding risk scores. <i>EuroIntervention<\/i> 2020; 16: 371\u2013379.","DOI":"10.4244\/EIJ-D-20-00052"},{"key":"6","doi-asserted-by":"crossref","unstructured":"6. 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Relationship between in-hospital event rates and high bleeding risk score in patients undergoing primary percutaneous coronary intervention for acute myocardial infarction. <i>Cardiovasc Interv Ther<\/i> 2022; 37: 490\u2013496.","DOI":"10.1007\/s12928-021-00805-3"},{"key":"9","doi-asserted-by":"crossref","unstructured":"9. Shimizu T, Sakuma Y, Kurosawa Y, Muto Y, Sato A, Abe S, et al. Validation of Japanese bleeding risk criteria in patients after percutaneous coronary intervention and comparison with contemporary bleeding risk criteria. <i>Circ Rep<\/i> 2022; 4: 230\u2013238.","DOI":"10.1253\/circrep.CR-22-0023"},{"key":"10","doi-asserted-by":"crossref","unstructured":"10. Nakanishi N, Kaikita K, Ishii M, Kuyama N, Tabata N, Ito M, et al. 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Causal machine learning for predicting treatment outcomes. <i>Nat Med<\/i> 2024; 30: 958\u2013968.","DOI":"10.1038\/s41591-024-02902-1"},{"key":"14","doi-asserted-by":"crossref","unstructured":"14. Mortazavi BJ, Bucholz EM, Desai NR, Huang C, Curtis JP, Masoudi FA, et al. Comparison of machine learning methods with national cardiovascular data registry models for prediction of risk of bleeding after percutaneous coronary intervention. <i>JAMA Netw Open<\/i> 2019; 2: e196835.","DOI":"10.1001\/jamanetworkopen.2019.6835"},{"key":"15","doi-asserted-by":"crossref","unstructured":"15. Zaka A, Mutahar D, Gorcilov J, Gupta AK, Kovoor JG, Stretton B, et al. Machine learning approaches for risk prediction after percutaneous coronary intervention: A systematic review and meta-analysis. <i>Eur Heart J Digit Health<\/i> 2025; 6: 23\u201344.","DOI":"10.1093\/ehjdh\/ztae074"},{"key":"16","doi-asserted-by":"crossref","unstructured":"16. Oba Y, Kabutoya T, Kohro T, Imai Y, Kario K, Sato H, et al. Relationships among heart rate, beta-blocker dosage, and prognosis in patients with coronary artery disease in a real-world database using a multimodal data acquisition system. <i>Circ J<\/i> 2023; 87: 336\u2013344.","DOI":"10.1253\/circj.CJ-22-0314"},{"key":"17","doi-asserted-by":"crossref","unstructured":"17. Matoba T, Kohro T, Fujita H, Nakayama M, Kiyosue A, Miyamoto Y, et al. Architecture of the Japan Ischemic Heart Disease Multimodal Prospective Data Acquisition for Precision Treatment (J-IMPACT) system. <i>Int Heart J<\/i> 2019; 60: 264\u2013270.","DOI":"10.1536\/ihj.18-113"},{"key":"18","doi-asserted-by":"crossref","unstructured":"18. Tokai T, Ishii M, Otsuka Y, Ikebe S, Nakamura T, Tsujita K, et al. Proton pump inhibitor use, gastrointestinal bleeding reduction, and long-term prognosis after percutaneous coronary intervention. <i>Am J Cardiol<\/i> 2025; 252: 78\u201387.","DOI":"10.1016\/j.amjcard.2025.05.037"},{"key":"19","doi-asserted-by":"crossref","unstructured":"19. 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SMOTE: Synthetic minority over-sampling technique. <i>J Artif Intell Res<\/i> 2002; 16: 321\u2013357.","DOI":"10.1613\/jair.953"},{"key":"25","doi-asserted-by":"crossref","unstructured":"25. Otsuka Y, Ishii M, Ikebe S, Nakamura T, Tsujita K, Kaikita K, et al. BNP level predicts bleeding event in patients with heart failure after percutaneous coronary intervention. <i>Open Heart<\/i> 2023; 10: e002489.","DOI":"10.1136\/openhrt-2023-002489"},{"key":"26","doi-asserted-by":"crossref","unstructured":"26. Kogame N, Nakagawa Y, Kozuma K, Iijima R, Tsutsui A, Murakami Y, et al. Sex-based differences in the effect of short dual antiplatelet therapy followed by prasugrel monotherapy in high-bleeding-risk patients after percutaneous coronary intervention. <i>Circ Rep<\/i> 2026; 8: 554\u2013563.","DOI":"10.1253\/circrep.CR-25-0298"},{"key":"27","doi-asserted-by":"crossref","unstructured":"27. Han JK, Lee SD, Hwang D, Park SH, Kang J, Yang HM, et al. 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Machine learning-based prediction of in-hospital death for patients with takotsubo syndrome: The InterTAK-ML model. <i>Eur J Heart Fail<\/i> 2023; 25: 2299\u20132311.","DOI":"10.1002\/ejhf.2983"}],"container-title":["Circulation Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/circrep\/8\/9\/8_CR-26-0116\/_pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,12]],"date-time":"2026-09-12T03:25:44Z","timestamp":1789183544000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/circrep\/8\/9\/8_CR-26-0116\/_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,10]]},"references-count":29,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2026]]}},"URL":"https:\/\/doi.org\/10.1253\/circrep.cr-26-0116","relation":{},"ISSN":["2434-0790"],"issn-type":[{"value":"2434-0790","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,10]]},"article-number":"CR-26-0116"}}