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使用明尼苏达州多相人格库存-2基于XGBoost机器学习算法诊断疼痛欺骗:单盲随机受控试验

Hyewon Chung¹, Kihwan Nam², Subin Lee¹

¹Department of Anesthesiology and Pain Medicine, College of Medicine, The Catholic University of Korea, Seoul 03312, Republic of Korea.

Medicina (Kaunas, Lithuania)

|January 8, 2025

概括

美国明尼苏达州多相人格库存-2 (MMPI-2) 的机器学习分析有效地检测出疼痛欺骗. 与传统方法相比,这种方法提供了更好的诊断准确性.

科学领域:

心理学心理学心理学
医疗信息学医疗信息学
机器学习机器学习

背景情况:

评估疼痛欺骗是很困难的,因为它的主观性质.
疼痛欺骗是一种心理干预形式,个人假装疼痛.
需要客观的诊断工具来欺骗疼痛.

研究的目的:

用机器学习 (ML) 分析明尼苏达州多相人格清单-2 (MMPI-2) 尺度来评估疼痛欺骗的诊断价值.
将ML诊断性能与后勤回归进行比较.
为了确定疼痛欺骗诊断的准确性,精度,回忆和f1分数.

主要方法:

一个单盲,随机对照试验,有96名参与者被分配到欺骗 (D) 和非欺骗 (ND) 组.
D组的参与者被教导假装疼痛.
应用了XGBoost ML算法来分析选择的MMPI-2尺度 (sMMPI-2).

主要成果:

后勤回归分析显示,对于疼痛或MMPI-2没有诊断价值.
对sMMPI-2尺度的ML分析实现了0.724.4的准确性.
在ML分析中,精度为0.692,回忆率为0.692,f1得分为0.692.

结论:

对MMPI-2数据的机器学习分析证明了对疼痛欺骗的诊断能力.
在诊断疼痛欺骗方面,ML优于传统的后勤回归.
考虑多个MMPI-2尺度和模式可以提高诊断准确度.

关键词:

这是MMPI的MMPI.欺骗欺骗欺骗后勤模型后勤模型机器学习是机器学习.这是模仿,模仿.疼痛疼痛疼痛疼痛人格测试的人格测试这是一种心理社会干预.唾液阿尔法-氨酸酶的使用

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