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使用更高阶物品响应理论和机器学习模型预测认知障碍

Lihua Yao¹, Yusuke Shono², Cindy Nowinski¹

¹Department of Medical Social Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL, United States.

Frontiers in psychiatry

|March 18, 2024

概括

机器学习模型改善了对老年人认知障碍 (CI) 的早期检测. 将高阶物品响应理论 (HOIRT) 评分与年龄等特征集成,可以比固定切断值更有效地预测.

科学领域:

神经科学是一个神经科学.
老年学是一门学科.
医疗保健中的人工智能

背景情况:

早期发现认知障碍 (CI) 对老年人的福祉至关重要.
现有的方法在识别CI个体时可能缺乏准确性.
验证的认知评估对于可靠的评估至关重要.

研究的目的:

探索机器学习 (ML) 模型,以提高CI的预测.
评估将更高阶物体响应理论 (HOIRT) 与 ML 整合的有效性.
确定老年人CI检测的最佳预测特征.

主要方法:

使用了两种NIH工具箱基于iPad的测量方法:图片序列记忆 (PSM) 和维度变化卡排序测试 (DCCS).
开发了一个2参数HOIRT模型,以创建一个综合得分,整合PSM和DCCS.
对比了包括Random Forest在内的各种ML模型,以预测86名患者的CI.

主要成果:

随机森林模型实现了高性能:准确度为0.902,精度为0.803,回忆率为0.758,F1得分为0.742,特异性为0.951.
性能最好的模型将HOIRT衍生的复合分数和年龄作为关键特征.
一个固定的复合分数截止值被发现不足以准确预测CI.

结论:

机器学习模型,特别是随机森林,显示出对预测CI的重大承诺.
将HOIRT衍生得分和相关的临床特征 (例如年龄) 整合起来,可以提高预测准确度.
这种方法为老年人早期CI检测和干预提供了更有效的策略.

关键词:

在此之前,IRT是IRT.这是我的CogCog.美国国立卫生研究院工具箱认知障碍是一种认知障碍.深度学习是一种深度学习.更高阶的项目响应理论.减值是一种减值.机器学习是机器学习.

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