Accuracy of diagnostic classification algorithms using cognitive-, electrophysiological-, and neuroanatomical data in antipsychotic-naïve schizophrenia patients.
Background: 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 dat...
| Publicado en: | Psychological Medicine Vol. 49; no. 16; pp. 2754 - 2764 |
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| Autores principales: | , , , , , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Cambridge University Press
Dec2019
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=139823463&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 139823463 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00332917 6Q3 jtl: Psychological Medicine issn: 00332917 maglogo: N pubinfo: dt: Dec2019 vid: 49 iid: 16 pid: 15979 pub: Cambridge University Press artinfo: ui: 139823463 139823463 139823463 10.1017/S0033291718003781 139823463 ppf: 2754 ppct: 10 formats: tig: atl: Accuracy of diagnostic classification algorithms using cognitive-, electrophysiological-, and neuroanatomical data in antipsychotic-naïve schizophrenia patients. aug: au: 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. affil: Centre for Neuropsychiatric Schizophrenia Research & Centre for Clinical Intervention and Neuropsychiatric Schizophrenia Research, Mental Health Centre Glostrup, University of Copenhagen, Copenhagen, Denmark sug: subj: Machine Learning Methods Classification Algorithms Schizophrenia Diagnosis Electrophysiology Cognition Evaluation Neuropsychological Tests Methods Magnetic Resonance Imaging Methods Schizophrenia Drug Therapy Antipsychotic Agents Therapeutic Use Human Conceptual Framework Benzamides Therapeutic Use Disease Remission Neuroanatomy Multivariate Analysis Treatment Outcomes Sensitivity and Specificity ab: Background: 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 as a framework for clinical translation. Methods: Forty-six antipsychotic-naïve, first-episode schizophrenia patients and 58 controls underwent neurocognitive tests, electrophysiology, and magnetic resonance imaging (MRI). Patients underwent clinical assessments before and after 6 weeks of antipsychotic monotherapy with amisulpride. Nine configurations of different supervised machine learning algorithms were applied to first estimate the unimodal diagnostic accuracy, and next to estimate the multimodal diagnostic accuracy. Finally, we explored the predictability of symptom remission. Results: Cognitive data significantly classified patients from controls (accuracies = 60–69%; p values = 0.0001–0.009). Accuracies of electrophysiology, structural MRI, and diffusion tensor imaging did not exceed chance level. Multimodal analyses with cognition plus any combination of one or more of the remaining three modalities did not outperform cognition alone. None of the modalities predicted symptom remission. Conclusions: In this multivariate and multimodal study in antipsychotic-naïve patients, only cognition significantly discriminated patients from controls, and no modality appeared to predict short-term symptom remission. Overall, these findings add to the increasing call for cognition to be included in the definition of schizophrenia. To bring about the full potential of machine learning algorithms in first-episode, antipsychotic-naïve schizophrenia patients, careful a priori variable selection based on independent data as well as inclusion of other modalities may be required. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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