Accuracy of diagnostic classification algorithms using cognitive-, electrophysiological-, and neuroanatomical data in antipsychotic-naïve schizophrenia patients

A wealth of clinical studies have identified objective biomarkers, which separate schizophrenia patients from healthy controls on a group level, but current diagnostic systems solely include clinical symptoms. In this study, we investigate if machine learning algorithms on multimodal data can serve...

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Bibliographic Details
Published in:	Psychological medicine Vol. 49; no. 16; pp. 2754 - 2763
Main Authors:	Ebdrup, Bjørn H., Axelsen, Martin C., Bak, Nikolaj, Fagerlund, Birgitte, Oranje, Bob, Raghava, Jayachandra M., Nielsen, Mette Ø., Rostrup, Egill, Hansen, Lars K., Glenthøj, Birte Y.
Format:	Journal Article
Language:	English
Published:	England Cambridge University Press 01.12.2019
Subjects:	Accuracy Alcohol Algorithms Amisulpride Anatomy Antipsychotics Biological markers Classification Clinical assessment Clinical trials Cognition Cognition & reasoning Cognitive ability Data Diagnostic systems Diffusion tensor imaging Electrophysiology Learning algorithms Machine learning Magnetic resonance imaging Medical diagnosis Mental disorders Multimodality Narcotics Original Patients Psychotropic drugs Remission Remission (Medicine) Schizophrenia Socioeconomic factors Translation Wealth Denmark structural magnetic resonance imaging Antipsychotic-naïve first-episode schizophrenia diffusion tensor imaging cognition electrophysiology machine learning
ISSN:	0033-2917, 1469-8978, 1469-8978
Online Access:	Get full text
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