使用机器学习来检测不可信的认知测试表现
John-Christopher A Finley1, Anthony D Robinson2, Jason R Soble2,3
1Department of Psychiatry and Behavioral Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
无监督机器学习 (ML) 在神经心理测试中准确评估了性能有效性. 这种使用患者数据的先进方法,有望改善神经心理学中的有效性评估和数字化整合.
科学领域:
- 神经心理学 神经心理学
- 人工智能的人工智能
- 数据科学数据科学数据科学
背景情况:
- 在神经心理测试中评估性能有效性至关重要.
- 先进的算法方法,包括机器学习 (ML),可以提高有效性评估.
研究的目的:
- 调查无监督ML在评估神经心理评估期间绩效有效性的有用性.
- 确定ML是否可以准确地将患者数据分为有效和无效的绩效组.
主要方法:
- 一个无监督的ML模型分析了359名成人门诊患者的数据.
- 数据包括表现有效性测试成绩,病史/精神病史,转诊原因,诉讼和残疾状况.
- 该模型的集群与已建立的有效性评级进行了比较.
主要成果:
- ML模型确定了两个集群:有效和无效的性能.
- 该模型实现了出色的预测准确性 (AUC = .92).
- 绩效有效性测试得分是最有影响力的,其次是病史,转诊原因和残疾状态.
结论:
- 无监督的ML可以使用各种神经心理评估数据准确评估性能有效性.
- 这种方法可以克服传统有效性评估方法的局限性.
- 无监督的ML可以适应数字技术,有可能改善未来的有效性评估.
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