Artificial neural network model for the prediction of obsessive-compulsive disorder treatment response

Autor: Paolo Cavedini, Patrizia Riva, Giuliana Salomoni, Massimiliano Grassi, Laura Bellodi, Paola Mosini
Přispěvatelé: Salomoni, G, Grassi, M, Mosini, P, Riva, P, Cavedini, P, Bellodi, Laura
Rok vydání: 2009
Předmět:
Oncology
Adult
Male
medicine.medical_specialty
Obsessive-Compulsive Disorder
medicine.medical_treatment
media_common.quotation_subject
Models
Neurological
Logistic regression
Risk Assessment
Behavior Therapy
Predictive Value of Tests
Risk Factors
Internal medicine
medicine
Humans
Pharmacology (medical)
Computer Simulation
Treatment Failure
Psychiatry
media_common
Retrospective Studies
Risperidone
Variables
Artificial neural network
Reproducibility of Results
Retrospective cohort study
Middle Aged
medicine.disease
Combined Modality Therapy
Exposure and response prevention
Psychiatry and Mental health
Logistic Models
Nonlinear Dynamics
Predictive value of tests
Drug Therapy
Combination
Female
Neural Networks
Computer
Serotonin Antagonists
Psychology
Factor Analysis
Statistical
Anxiety disorder
Selective Serotonin Reuptake Inhibitors
medicine.drug
Zdroj: Journal of clinical psychopharmacology. 29(4)
ISSN: 1533-712X
Popis: Several patients with obsessive-compulsive disorder (OCD) who are refractory to adequate treatment with first-line treatments are considered treatment-resistant. Further surveys were to be implemented to explore the outcome predictors of the antiobsessional response. Such study was aimed at building a model suitable to predict the final outcome of a mixed OCD pharmacologic and nonpharmacologic treatment approaches. We studied 130 subjects with OCD who underwent pharmacologic (with selective serotonin reuptake inhibitors alone or with selective serotonin reuptake inhibitors and risperidone at low dosage) and/or behavioral therapy (using exposure and response prevention techniques). The following variables were used as predictors: symptoms dimension, as resulting from the Yale-Brown Obsessive-Compulsive Scale items factor analysis; neuropsychologic performances; and epidemiologic variables. The treatment response arising from 3 to 6 months of therapy was used as dependent variable. A conventional logistic regression was used to define a previsional model of treatment response and multilayer perceptrons and to supervise an artificial neural network technique. The 46.9% of the sample resulted to be refractory to treatment. Results obtained with the logistic regression model showed that the only predictors of treatment outcome are hoarding symptoms, repeating rituals, and counting compulsions. Furthermore, using all the variables considered in the models, multilayer perceptrons showed highly better predictive performance as compared with the logistic regression models (93.3% vs 61.5%, respectively, of correct classification of cases). Complex interactions between different clinical and neuropsychologic variables are involved in defining OCD treatment response profile, and nonlinear and interactive modeling strategies, that is, supervised artificial neural networks, seem to be more suitable to investigate this complexity than linear techniques.
Databáze: OpenAIRE
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