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在慢性心力衰竭患者中基于机器学习的轻度认知障碍风险预测:模型开发和验证研究

Jin Yang¹, Yan Xie², Tianyi Wang²

¹Chengdu Medical College, China; Department of Cardiology, Sichuan Mianyang 404 Hospital, China.

Geriatric nursing (New York, N.Y.)

|February 2, 2025

概括

这项研究开发了一种机器学习模型,用于预测慢性心力衰竭 (CHF) 患者的轻度认知障碍 (MCI) 风险. XGBoost模型准确地识别了高风险个体,有助于早期查,并可能减少住院治疗.

科学领域:

心脏病学心脏病学
神经学神经学
人工智能的人工智能

背景情况:

慢性心力衰竭 (CHF) 患者面临轻度认知障碍 (MCI) 的风险增加,影响再住院和死亡率.
在CHF患者中早期识别MCI对于及时干预和改善患者结果至关重要.

研究的目的:

开发和验证用于预测慢性心力衰竭患者MCI风险的机器学习模型.
在这个患者群体中确定MCI的关键预测因素.

主要方法:

一项横截面分析包括602名慢性心力衰竭患者.
用AUC,校准和决策曲线构建和评估了四个机器学习模型.
关键预测因素包括心理/社会适应,年龄,自由三甲状腺素,抑郁症得分,血红蛋白,睡眠时间和性别.

主要成果:

极端梯度增强 (XGBoost) 模型表现出卓越的性能,其AUC为0.940.
心理/社会适应,年龄,自由三甲状腺素,抑郁症,血红蛋白,睡眠和性别被确定为重要的预测因素.
开发了一种动态的诺姆图,用于大群体的早期风险查.

结论:

XGBoost机器学习模型有效预测慢性心力衰竭患者的MCI风险.
开发的动态名录图有助于早期查和风险分层.
准确的MCI风险预测可以指导干预措施,以减少CHF患者的再住院和死亡率.

关键词:

慢性心力衰竭慢性心力衰竭认知障碍认知障碍是一种认知障碍.在MCI中,MCI是MCI.机器学习是机器学习.预测模型是一个预测模型.

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