Applying artificial neural network models to clinical decision making

被引:33
|
作者
Price, RK
Spitznagel, EL
Downey, TJ
Meyer, DJ
Risk, NK
El-Ghazzawy, OG
机构
[1] Washington Univ, Sch Med, Dept Psychiat, St Louis, MO 63108 USA
[2] Washington Univ, Dept Math, St Louis, MO 63108 USA
[3] Partek Inc, St Peters, MO USA
[4] Washington Univ, Dept Chem, St Louis, MO 63108 USA
关键词
D O I
10.1037/1040-3590.12.1.40
中图分类号
B849 [应用心理学];
学科分类号
040203 ;
摘要
Because psychological assessment typically lacks biological gold standards, it traditionally has relied on clinicians' expert knowledge. A more empirically based approach frequently has applied linear models to data to derive meaningful constructs and appropriate measures. Statistical inferences are then used to assess the generality of the findings. This article introduces artificial neural networks (ANNs), flexible nonlinear modeling techniques that test a model's generality by applying its estimates against "future" data. ANNs have potential for overcoming some shortcomings of linear models. The basics of ANNs and their applications to psychological assessment are reviewed. Two examples of clinical decision making are described in which an ANN is compared with linear models, and the complexity of the network performance is examined. Issues salient to psychological assessment are addressed.
引用
收藏
页码:40 / 51
页数:12
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