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Individualized prediction of illness course at the first psychotic episode: a support vector machine MRI study.

dc.contributor.authorMourao-Miranda, J
dc.contributor.authorReinders, A A T S
dc.contributor.authorRocha-Rego, V
dc.contributor.authorLappin, J
dc.contributor.authorRondina, J
dc.contributor.authorMorgan, C
dc.contributor.authorMorgan, K D
dc.contributor.authorFearon, P
dc.contributor.authorJones, P B
dc.contributor.authorDoody, G A
dc.contributor.authorMurray, R M
dc.contributor.authorKapur, S
dc.contributor.authorDazzan, P
dc.date.accessioned2012-09-17T09:01:15Z
dc.date.available2012-09-17T09:01:15Z
dc.date.issued2012-05
dc.identifier.citationIndividualized prediction of illness course at the first psychotic episode: a support vector machine MRI study. 2012, 42 (5):1037-47 Psychol Meden_GB
dc.identifier.issn1469-8978
dc.identifier.pmid22059690
dc.identifier.doi10.1017/S0033291711002005
dc.identifier.urihttp://hdl.handle.net/10147/244207
dc.description.abstractTo date, magnetic resonance imaging (MRI) has made little impact on the diagnosis and monitoring of psychoses in individual patients. In this study, we used a support vector machine (SVM) whole-brain classification approach to predict future illness course at the individual level from MRI data obtained at the first psychotic episode.
dc.language.isoenen
dc.relation.urlhttp://journals.cambridge.org/download.php?file=%2F25539_0E81A2842381C7988A0DAD68AE8195F9_journals__PSM_PSM42_05_S0033291711002005a.pdf&cover=Y&code=dc5e5e7db712fa74d298f403f4d38dc7en_GB
dc.relation.urlhttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC3315786/pdf/S0033291711002005a.pdfen_GB
dc.rightsArchived with thanks to Psychological medicineen_GB
dc.subject.meshAdult
dc.subject.meshBrain
dc.subject.meshBrain Mapping
dc.subject.meshCohort Studies
dc.subject.meshDisease Progression
dc.subject.meshFemale
dc.subject.meshFollow-Up Studies
dc.subject.meshHumans
dc.subject.meshImage Processing, Computer-Assisted
dc.subject.meshIndividuality
dc.subject.meshMagnetic Resonance Imaging
dc.subject.meshMale
dc.subject.meshObserver Variation
dc.subject.meshPredictive Value of Tests
dc.subject.meshPsychotic Disorders
dc.subject.meshReproducibility of Results
dc.subject.meshSupport Vector Machines
dc.titleIndividualized prediction of illness course at the first psychotic episode: a support vector machine MRI study.en_GB
dc.typeArticleen
dc.contributor.departmentCentre for Neuroimaging Sciences, Institute of Psychiatry, King's College London, UK.en_GB
dc.identifier.journalPsychological medicineen_GB
dc.description.provinceLeinsteren
html.description.abstractTo date, magnetic resonance imaging (MRI) has made little impact on the diagnosis and monitoring of psychoses in individual patients. In this study, we used a support vector machine (SVM) whole-brain classification approach to predict future illness course at the individual level from MRI data obtained at the first psychotic episode.


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