A pilot study to determine whether machine learning methodologies using pre-treatment electroencephalography can predict the symptomatic response to clozapine therapy

• Published in: Clinical neurophysiology Vol. 121; no. 12; pp. 1998 - 2006
• Main Authors: Khodayari-Rostamabad, Ahmad, Hasey, Gary M., MacCrimmon, Duncan J., Reilly, James P., Bruin, Hubert de
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ireland Ltd 01.12.2010; Elsevier
• Subjects: Adult; Adult and adolescent clinical studies; Antipsychotic Agents - therapeutic use; Artificial Intelligence; Biological and medical sciences; Clozapine; Clozapine - therapeutic use; Discrimination (Psychology); EEG; Electrodiagnosis. Electric activity recording; Electroencephalography - methods; Female; Follow-Up Studies; Humans; Investigative techniques, diagnostic techniques (general aspects); Machine learning; Male; Medical sciences; Middle Aged; Nervous system; Neurology; Pilot Projects; Predictive Value of Tests; Psychiatric Status Rating Scales; Psychiatry; Psychology. Psychoanalysis. Psychiatry; Psychopathology. Psychiatry; Psychoses; Reproducibility of Results; Schizophrenia; Schizophrenia - drug therapy; Schizophrenia - physiopathology; Sensitivity and Specificity; Treatment Outcome; Treatment-efficacy prediction; Schizophrenia; Clozapine; Psychiatry; EEG; Treatment-efficacy prediction; Machine learning; Human; Minimum; Methodology; Selection; Prediction; Electrophysiology; Electroencephalography; Representation; Kernel method; Psychosis; Learning; Chronic; Acquisition process; Treatment; Least squares method; Performance

To investigate whether applying advanced machine learning (ML) methodologies to pre-treatment electroencephalography (EEG) data can predict the response to clozapine therapy in adult subjects suffering from chronic schizophrenia. Pre-treatment EEG data are collected in 23 + 14 schizophrenic adults....