摘要
认知功能障碍是影响维持性血液透析患者生活质量和病死率的重要影响因素。建立预测模型可以帮助医务人员提前发现认知功能障碍和识别风险,为患者早期干预提供时间窗。相关预测模型的构建主要依靠传统统计学,而近年来,随着人工智能技术的迅速发展,极大地推进了其在相关领域的应用。本文总结比较了传统统计学与机器学习的特点以及其在研究血液透析患者认知功能障碍方面的临床应用,为建立预测维持性血液透析患者认知功能障碍模型提供参考和借鉴。
Cognitive impairment constitutes a crucial influencing factor affecting the quality of life and mortality rates of maintenance hemodialysis patients.Predictive modeling can facilitate the early detection of cognitive impairment and the identification of risks,thereby providing a time window for early interventions.Usually,the construction of prediction models predominantly relies on traditional sta⁃tistics.In recent years,with the rapid advancement of artificial intelligence technology,its application in the field of nephrology has witnessed a significant boost.This article comprehensively summarized and compared the characteristics of traditional statistics and machine learning,as well as their clinical applica⁃tions to the research of cognitive impairment in hemodialysis patients,offering valuable references for building prediction models.
作者
王冰莹
王楠
Wang Bing-ying;Wang Nan(Graduate School of Dalian Medical University,Dalian 116051,China;Department of Nephrology,the First Affiliated Hospital of Dalian Medical University,Dalian 116011,China)
出处
《临床肾脏病杂志》
2025年第9期798-802,共5页
Journal of Clinical Nephrology
关键词
人工智能
机器学习
认知障碍
血液透析
预测模型
Artificial intelligence
Machine learning
Cognitive impairment
Hemodialysis
Fore⁃casting model
