人工智能在心血管–肾脏–代谢综合征临床诊断、治疗与管理中的应用
Application of Artificial Intelligence in Clinical Diagnosis, Treatment and Management of Cardiovascular‑Kidney‑Metabolic Syndrome
DOI: 10.12677/jcpm.2026.51084, PDF,    科研立项经费支持
作者: 李 霄, 庄向华*：山东大学齐鲁第二医院内分泌代谢科，山东 济南；赵昌盛：山东大学齐鲁第二医院营养科，山东 济南；张栋栋：山东大学齐鲁第二医院消化内科，山东 济南
关键词: 人工智能；心血管–肾脏–代谢综合征；机器学习；精准医疗；慢病管理；Artificial Intelligence； Cardiovascular-Kidney-Metabolic Syndrome； Machine Learning； Precision Medicine； Chronic Disease Management
摘要: 心血管–肾脏–代谢(CKM)综合征作为代谢紊乱与心血管疾病、慢性肾脏病相互作用引发的复杂临床综合征，其共病发生率持续逐年升高，已成为当前全球公共卫生领域面临的严峻挑战。传统医疗模式在多源数据整合处理以及个体化精准诊疗决策制定等方面存在较为显著的局限性，而人工智能(AI)借助高效的数据挖掘能力与精准预测建模优势为CKM综合征全程诊疗管理提供了创新解决路径。本文系统梳理总结AI在CKM综合征风险预测、早期诊断、个体化治疗及长期慢病管理全周期中的相关应用与研究进展，深入探讨当前该领域应用实践中面临的关键问题并对未来发展趋势作出展望，为促进AI技术在CKM综合征临床领域的切实落地应用提供参考依据。
Abstract: Cardiovascular-renal-metabolic (CKM) syndrome is a complex clinical syndrome caused by the interaction of metabolic disorders, cardiovascular disease and chronic kidney disease. The incidence of comorbidity continues to increase year by year, which has become a serious challenge in the field of global public health. The traditional medical model has significant limitations in multi-source data integration processing and individualized precision diagnosis and treatment decision-making, while artificial intelligence (AI) provides an innovative solution for the whole-course diagnosis and treatment management of CKM syndrome with the advantages of efficient data mining ability and accurate prediction modeling. This paper systematically summarizes the application and research progress of AI in the whole cycle of risk prediction, early diagnosis, individualized treatment and long-term chronic disease management of CKM syndrome, discusses the key problems in the current application practice in this field and looks forward to the future development trend, so as to provide reference for promoting the practical application of AI technology in the clinical field of CKM syndrome.
文章引用：李霄, 赵昌盛, 张栋栋, 庄向华. 人工智能在心血管–肾脏–代谢综合征临床诊断、治疗与管理中的应用[J]. 临床个性化医学, 2026, 5(1): 611-621. https://doi.org/10.12677/jcpm.2026.51084

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