An artificial neural network that uses eye-tracking performance to identify patients with schizophrenia
An artificial neural network that uses eye-tracking performance to identify patients with schizophrenia
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DOI：
10.1093/oxfordjournals.schbul.a033419
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发表时间：
1999-01-01
影响因子：
6.6
通讯作者：
Scarone, S
中科院分区：
文献类型：
--
作者：
Campana, A;Duci, A;Scarone, S
一些研究人员强调了精确表征精神分裂症患者眼动追踪功能障碍（ETD）的重要性。这种生物学特征似乎有助于估计个体基因重组的可能性，因此它可能有助于连锁研究。本文描述了一种使用 ETD 识别精神分裂症的非线性计算模型，使用反向传播神经网络 (BPNN) 根据精神分裂症患者和正常对照受试者的眼动追踪表现对他们进行分类。使用 BPNN 获得的分类结果比线性计算模型（判别分析）更好：先验预测的正确率约为 80%。这些结果首先表明，眼球追踪模式可用于区分精神分裂症患者与正常对照组，准确率约为 80%。其次，并行分布式处理网络能够检测眼动追踪性能的预测定量测量之间的高阶非线性关系。
Several researchers have underscored the importance of precise characterization of eye-tracking dysfunction (ETD) in patients with schizophrenia. This biological trait appears to be useful in estimating the probability of genetic recombination in an individual, so it may be helpful in linkage studies. This article describes a nonlinear computational model for using ETD to identify schizophrenia, A back-propagation neural network (BPNN) was used to classify schizophrenia patients and normal control subjects on the basis of their eye-tracking performance, Better classification results were obtained with BPNN than with a linear computational model (discriminant analysis): a priori predictions were approximately 80 percent correct. These results suggest, first, that eye-tracking patterns can be useful in distinguishing patients with schizophrenia from a normal comparison group with an accuracy of approximately 80 percent. Second, parallel distributed processing networks are able to detect higher order nonlinear relationships among predictor quantitative measurements of eye-tracking performance.