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Appendix C: 参考文献与数据来源

References

[Ge2026] Zheng P, Yan W, Ding Y, et al., Ge J (corresponding author). Cardiovascular ageing: hallmarks, signalling pathways, diseases and therapeutic targets. Signal Transduction and Targeted Therapy. 2026;11:142. doi:10.1038/s41392-026-02630-7. 注：本书中以 [Ge2026] 引用，Ge J 为通讯作者。(核心理论框架，本书所有心血管衰老与心衰机制分析均基于此文的三层脚手架模型与12项标志)

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  2. Rajkomar A, Oren E, Chen K, et al. Scalable and accurate deep learning with electronic health records. npj Digital Medicine. 2018;1:18.

  3. Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nature Medicine. 2019;25(1):44-56.

  4. Choi E, Bahadori MT, Schuetz A, Stewart WF, Sun J. Doctor AI: Predicting clinical events via recurrent neural networks. arXiv preprint arXiv:1511.05942. 2015.

  5. Pham T, Tran T, Phung D, Venkatesh S. DeepCare: A deep dynamic memory model for predictive medicine. Pacific-Asia Conference on Knowledge Discovery and Data Mining. 2016:30-41.

  6. Huang K, Altosaar J, Ranganath R. ClinicalBERT: Modeling clinical notes and predicting hospital readmission. arXiv preprint arXiv:1904.05342. 2019.

  7. Singhal K, Azizi S, Tu T, et al. Large language models encode clinical knowledge. Nature. 2023;620(7972):172-180.

  8. Yang X, Chen A, PourNejatian N, et al. A large language model for electronic health records. npj Digital Medicine. 2022;5:194.

  9. Saab K, Tu T, Weng WH, et al. Capabilities of Gemini models in medicine. arXiv preprint arXiv:2404.18425. 2024.

  10. Vayena E, Blasimme A, Cohen IG. Machine learning in medicine: Addressing ethical challenges. PLoS Medicine. 2018;15(11):e1002689.

  11. Price WN, Cohen IG. Privacy in the age of medical big data. Nature Medicine. 2019;25(1):37-43.

  12. Bruyninckx H, et al. Digital twins in healthcare: A systematic review. npj Digital Medicine. 2024;7:45.

  13. Corral-Acero J, Margara F, Marciniak M, et al. The 'Digital Twin' to enable the vision of precision cardiology. European Heart Journal. 2020;41(48):4556-4564.

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  15. Brugts JJ, Radhoe SP, Clephas PRD, et al. Remote haemodynamic monitoring of pulmonary artery pressures in patients with chronic heart failure (MONITOR-HF): a randomised clinical trial. The Lancet. 2023;401(10394):2113-2123.

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  19. Shah SJ, Katz DH, Selvaraj S, et al. Phenomapping for novel classification of heart failure with preserved ejection fraction. Circulation. 2015;131(3):269-279.

  20. Reddy YNV, Carter RE, Obokata M, Redfield MM, Borlaug BA. A simple, evidence-based approach to help guide diagnosis of heart failure with preserved ejection fraction. Circulation. 2018;138(9):861-870.

  21. Attia ZI, Friedman PA, Noseworthy PA, et al. Age and sex estimation using artificial intelligence from standard 12-lead ECGs. Circulation: Arrhythmia and Electrophysiology. 2019;12(9):e007284.

  22. McMurray JJV, Packer M, Desai AS, et al. Angiotensin-neprilysin inhibition versus enalapril in heart failure. New England Journal of Medicine. 2014;371(11):993-1004.

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  36. Mandel JC, Kreda DA, Mandl KD, Kohane IS, Ramoni RB. SMART on FHIR: A standards-based, interoperable apps platform for electronic health records. Journal of the American Medical Informatics Association. 2016;23(5):899-908.

  37. D'Amore JD, Mandel JC, Kreda DA, et al. Are meaningful use stage 2 certified EHRs ready for interoperability? Findings from the SMART C-CDA collaborative. AMIA Annual Symposium Proceedings. 2014;2014:458-467.

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  39. Kaplan RS, Porter ME. How to solve the cost crisis in health care. Harvard Business Review. 2011;89(9):46-52.

  40. Kruk ME, Gage AD, Arsenault C, et al. High-quality health systems in the Sustainable Development Goals era: Time for a revolution. The Lancet Global Health. 2018;6(11):e1196-e1252.

  41. Odekunle FF, Odekunle RO. The role of artificial intelligence in healthcare in low-resource settings: A systematic review. Journal of Global Health. 2023;13:04045.

Data Sources

  • Global heart failure epidemiology data: Global Burden of Disease Study 2023（IHME GBD 2023，访问日期：2026 年 6 月；数据字段包括心衰发病率、患病率、伤残调整寿命年（DALYs）等）
  • Clinical trial data: ClinicalTrials.gov, PubMed, and sponsor publications
  • Regulatory frameworks: FDA AI/ML-Based SaMD Action Plan, EMA guidance on AI in medicinal products

All references are cited in order of first appearance in the text. Vancouver style is used throughout.