Past Issues

Background: Artifi cial neural network (ANN) can offer specifi c advantages
with respect to classical statistical techniques. This article is designed to acquaint
psychiatrists with concepts and paradigms related to ANN. Method: Literature
dealing with pharmacological prediction of depression and schizophrenia was reviewed.
Results: In most studies, ANN was found to have similar or better predictive
performance than logistic regression. Models combining clinical and genetic
data had a higher predictive accuracy than those using clinical data alone. Family
of ANN, when appropriately selected and used, permits the maximization of what
can be derived from available data and from complex, dynamic, and multidimensional
psychopharmacological data. Conclusion: Future prospective studies can
use the ANN models in real-life, and diverse clinical settings are critical in determining
whether this type of system will have important clinical impact on patient
outcome.