Relative accuracy of three automated systems for neuropsychological interpretation.

K M Adams, V I Kvale, J F Keegan
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引用次数: 20

Abstract

Three computer programs for automated interpretation of variants of the Halstead-Reitan Neuropsychological Test Battery were tested on cases in two data sets. The Key approach of Russell, Neuringer, and Goldstein (1970), Brain I (Finkelstein, 1977), and Adams' (1975) ability-based algorithm were employed in the study. The first data set included 63 well-documented cases with precise criterion data and multiple sources of direct verification. The second data set consisted of 30 equally well-studied cerebrovascular disease patients whose cerebral circulation disorders resulted in clinical manifestations encompassing the entire range of stroke. Results suggested that, while none of the programs do poorly at identifying the presence of brain damage, lateralization and possibly other localization/process predictions are not done well by these programs. The failure described in this particular study does not imply that automated methods are potentially less effective than true actuarial or clinical ones. Rather, we suggest that the translation process from clinical interpretation to the mechanical combinatory logic of the digital computer is at an early stage.

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神经心理学解释的三种自动化系统的相对准确性。
在两个数据集的案例中测试了三个用于自动解释Halstead-Reitan神经心理测试电池变体的计算机程序。本研究采用了Russell, Neuringer, and Goldstein(1970)的Key approach, Brain I (Finkelstein, 1977)和Adams(1975)的基于能力的算法。第一组数据包括63个记录良好的病例,具有精确的标准数据和多个直接验证来源。第二组数据包括30名同样经过充分研究的脑血管疾病患者,这些患者的脑循环障碍导致的临床表现涵盖了整个中风范围。结果表明,虽然没有一个程序在识别脑损伤的存在方面做得不好,但这些程序在侧化和可能的其他定位/过程预测方面做得不好。本研究中描述的失败并不意味着自动化方法可能不如真正的精算或临床方法有效。相反,我们认为从临床解释到数字计算机的机械组合逻辑的翻译过程尚处于早期阶段。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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