Applying artificial neural network models to clinical decision making.
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.
Price
R K
RK
Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri 63108, USA. price@rkp.wustl.edu
Spitznagel
E L
EL
Downey
T J
TJ
Meyer
D J
DJ
Risk
N K
NK
el-Ghazzawy
O G
OG
D000293:Adolescent; D000328:Adult; D000368:Aged; D000987:Antisocial Personality Disorder / Q000175:diagnosis; D002648:Child; D003658:Decision Making, Computer-Assisted; D003936:Diagnosis, Computer-Assisted / Q000706:statistics & numerical data*; D006801:Humans; D008875:Middle Aged; D016571:Neural Networks, Computer; D010552:Personality Assessment / Q000706:statistics & numerical data*; D011379:Prognosis; D011594:Psychometrics; D015203:Reproducibility of Results; D019966:Substance-Related Disorders / Q000175:diagnosis / Q000401:mortality; D014728:Veterans / Q000523:psychology / Q000706:statistics & numerical data