Abstract

Introduction

Objective neuropsychology test score pattern matching methods can help to identify data similarities and differences with comparison groups which can help the clinician in diagnosis and in identifying treatment options.

Materials and methods

The current study examines five methods of matching a data set: Correlation, Configuration, Kullback–Leibler (KL) Divergence, Pooled Effect Size (Cohen’s d), and a new method called MNB (Meyers Neuropsychological Battery) Code. Thirty data sets diagnosed with Traumatic Brain Injury (TBI) were compared with four Comparison Group data sets consisting of TBI, Depression, Anxiety and Attention Deficit/Hyperactivity Disorder.

Results

The Correlation Method was correct 90% (27/30) and Configuration was correct 86% (26/30). The KL Divergence was correct 76% (23/30) and the MNB Code was correct 73% (22/30). The Effect Size Method was correct 70% (21/30). When using a simple majority of all the matching methods, the classification rate was 90+ percent.

Conclusions

The results of this study demonstrate that there are statistical methods that can identify patterns of cognitive strengths and weaknesses. Multiple matching methods and a simple majority of agreement between the different comparisons suggests the best matching profile for diagnosis. In some cases, more than one pattern may be present.

中文翻译：

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匹配神经心理学模式的客观方法：公式和比较

摘要

介绍

客观的神经心理学测试分数模式匹配方法可以帮助识别数据与比较组的相似性和差异性，这可以帮助临床医生进行诊断和确定治疗方案。

材料和方法

当前的研究检查了五种匹配数据集的方法：相关性、配置、Kullback–Leibler (KL) 散度、合并效应量 (Cohen's d ) 和一种称为 MNB（迈耶斯神经心理学电池）代码的新方法。将 30 个诊断为创伤性脑损伤 (TBI) 的数据集与由 TBI、抑郁、焦虑和注意力缺陷/多动障碍组成的四个比较组数据集进行了比较。

结果

关联方法正确率为 90% (27/30)，配置正确率为 86% (26/30)。KL 散度的正确率为 76% (23/30)，而 MNB 代码的正确率为 73% (22/30)。效应大小法的正确率为 70% (21/30)。当使用所有匹配方法中的简单多数时，分类率为 90% 以上。

结论

这项研究的结果表明，有一些统计方法可以识别认知优势和劣势的模式。多种匹配方法和不同比较之间的简单多数协议表明诊断的最佳匹配配置文件。在某些情况下，可能存在不止一种模式。