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开发基于机器学习的模型,用于检测接受维护血液透析的患者的认知障碍

Tsai-Chieh Ling¹, Chiung-Chih Chang², Jia-Ling Wu^1,3

¹Department of Internal Medicine, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan.

European journal of neurology

|June 14, 2025

概括

机器学习模型使用简短的认知测试准确地选透析患者的认知障碍. 这种快速选工具可以有效地识别有风险的个人.

科学领域:

腎臟病學 (nephrology) 是一種醫學.
神经学神经学
数据科学数据科学数据科学

背景情况:

认知障碍很普遍,但在血液透析患者中往往没有被诊断出来.
早期检测对于及时干预和改善患者结果至关重要.

研究的目的:

开发和验证一种快速,准确的查工具,用于血液透析患者的认知障碍.
利用机器学习来有效识别认知能力下降.

主要方法:

一项涉及508名血液透析患者的横截面研究.
使用了迷你精神状态检查 (MMSE) 和认知能力查仪器 (CASI) 数据.
使用关键的MMSE/CASI项目开发和比较了六个机器学习模型 (Lasso,CART,RF,XGBoost,SVM,ANN).

主要成果:

大约40%的参与者表现出认知障碍 (CASI得分低于20百分位).
随机森林 (RF) 模型表现出卓越的预测性能,准确度为0.94,AUC为0.95,F1得分为0.92.
大多数模型,不包括CART,显示了可比的AUC值.

结论:

机器学习模型可以有效地选血液透析患者的认知障碍,使用MMSE和CASI的一些问题.
这种方法可以快速 (不到5分钟) 准确地识别这一群体的认知缺陷.

关键词:

认知障碍是一种认知障碍.深度学习是一种深度学习.痴呆症痴呆症是一种痴呆症.血液透析是血液透析的方法之一.机器学习是机器学习.

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