Artificial neural networks: a study in clinical psychopharmacology

Controlled trials in clinical psychopharmacology may fail to provide reliab le information about the benefit of treatment when the patient is viewed in a real-life setting rather than as part of a well-defined sampling procedu re. A viewpoint, rooted in systems theory, is proposed based on the identif ication of complex relationships among such dimensions as clinician's reaso ning, drug properties, and patient's condition. Artificial Neural Network ( ANN) technology provides efficient tools for data analysis within a systems -oriented approach. This study proposes a way to predict the outcome of psy chopharmacological treatment. Analysis was conducted on retrospective data from clinical records of psychiatric patients treated with moclobemide. Twe lve pharmacological, diagnostic, and topological variables were identified as the decisional items considered by six clinicians: age at onset, sex, pr evious treatment, duration and dose of moclobemide treatment, other drugs, psychiatric diagnosis and other clinical features. Data were binarily coded and transformed into observed frequencies in the sampling space; treatment outcome was binarily scored as the model's target. A Back-Propagation ANN based on the Delta rule with logistic transfer function was used. ANN corre ctly classified all cases of successful treatment (n = 51, 100%) but only h alf of the unsuccessful cases (n = 14, 52%). Patterns of response and areas of uncertainty were analyzed in a topological approach. (C) 1999 Elsevier Science Ireland Ltd. All rights reserved.