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中华肾病研究电子杂志 ›› 2025, Vol. 14 ›› Issue (04) : 181 -187. doi: 10.3877/cma.j.issn.2095-3216.2025.04.001

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机器学习:肾脏疾病研究与诊疗的新前沿
李媛媛, 李荣山()   
  1. 030012 太原,山西省人民医院肾内科
  • 收稿日期:2024-12-25 出版日期:2025-08-28
  • 通信作者: 李荣山
  • 基金资助:
    教育部慢性肾脏病医药基础研究创新中心(山西医科大学)专项基金(CKD/SXMU-2024-04); 山西省基础研究计划(202103021224388); 山西省中医药管理局科研课题(2024ZYYA015)

Machine learning: a new frontier in research and therapy of kidney diseases

Yuanyuan Li, Rongshan Li()   

  1. Department of Nephrology, Shanxi Provincial People′s Hospital, Taiyuan 030012, Shanxi Province, China
  • Received:2024-12-25 Published:2025-08-28
  • Corresponding author: Rongshan Li
引用本文:

李媛媛, 李荣山. 机器学习:肾脏疾病研究与诊疗的新前沿[J/OL]. 中华肾病研究电子杂志, 2025, 14(04): 181-187.

Yuanyuan Li, Rongshan Li. Machine learning: a new frontier in research and therapy of kidney diseases[J/OL]. Chinese Journal of Kidney Disease Investigation(Electronic Edition), 2025, 14(04): 181-187.

机器学习作为人工智能的一个重要分支,在肾脏疾病的研究和临床工作中展现出巨大潜力。机器学习,可用于分析肾脏疾病的临床、病理、医学影像和多组学等数据,助力肾脏疾病的早期诊断、疗效预测、发病机制探索等研究。机器学习可基于不同类型的肾脏疾病数据库用于构建相应模型,从而在肾脏疾病的风险因素分析、疾病诊断、药物疗效预估等方面发挥重要作用。本文对机器学习在肾脏病学领域的作用进行综述,为肾脏疾病的研究与诊疗提供新的视角。

Machine learning, as a crucial branch of artificial intelligence, has demonstrated tremendous potential in the research and clinical work of kidney diseases. Machine learning can be utilized to analyze clinical, pathological, medical imaging, and multi-omics data related to kidney diseases, facilitating early diagnosis, efficacy prediction, exploration of pathogenesis, and other related research. Machine learning can be used to construct corresponding models based on different types of kidney disease databases, thereby playing a significant role in risk factor analysis, disease diagnosis, and drug efficacy prediction of kidney diseases. This article provided a comprehensive review of the role of machine learning in nephrology, offering a fresh perspective for the research, diagnosis, and treatment of kidney diseases.

图1 机器学习算法学习方式分类注:机器学习算法可以依据学习方式分为监督学习、无监督学习和强化学习;监督学习是从有标记的数据集中推导出预测函数,即给定数据来预测标签;无监督学习用于推导预测函数的数据集是无标记的;强化学习是让模型从数据集中学习到根据当前状态选择最优的行为,对于输入和输出构建动态交互关系
图2 机器学习在肾脏疾病研究中的基本流程
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