摘要慢性肾脏病已成为全球性重要公共卫生问题，为医疗卫生体系带来沉重负担，有必要对肾脏疾病进行监测，开展早期干预与管理。由于我国肾脏疾病特征存在较大区域差异并且医疗资源分布尚不均衡，传统的疾病监测方式难以全面探究我国肾脏疾病的人群特征和流行趋势。通过将大数据、人工智能与疾病监测深度融合，在保障数据安全与个人隐私的前提下，适度整合不同来源的健康医疗数据，可以克服传统监测模式的诸多弊端，建立符合成本-效益比的肾脏疾病监测系统，从而为我国肾脏疾病防控提供参考依据。

abstractsChronic kidney disease is increasingly recognized as an important global public health problem, posing a heavy burden to the health system. It is necessary to monitor the status of kidney diseases and promote early intervention and management. Due to the large regional differences in the characteristics of kidney diseases and the uneven distribution of medical resources in China, traditional monitoring methods have several limitations in comprehensively exploring the burden and trends of kidney diseases. On the premise of ensuring data security and personal privacy, a cost-effective kidney disease surveillance system could be developed by integrating big data, artificial intelligence, and surveillance systems and utilizing health care data from different sources, thereby overcoming major disadvantages of traditional monitoring methods and providing reference for the prevention and control of kidney diseases in China.