S12. A MACHINE LEARNING FRAMEWORK FOR ROBUST AND RELIABLE PREDICTION OF SHORT- AND LONG-TERM CLINICAL RESPONSE IN INITIALLY ANTIPSYCHOTIC-NAïVE SCHIZOPHRENIA PATIENTS BASED ON MULTIMODAL NEUROPSYCHIATRIC DATA

BACKGROUND: The treatment response of patients with schizophrenia is heterogeneous, and markers of clinical response are missing. Studies using machine learning approaches have provided encouraging results regarding prediction of outcomes, but replicability has been challenging. In the present study...

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Detalles Bibliográficos
Autores principales: Ambrosen, Karen S, Skjerbæk, Martin W, Foldager, Jonathan, Axelsen, Martin C, Bak, Nikolaj, Arvastson, Lars, Christensen, Søren R, Johansen, Louise B, Raghava, Jayachandra M, Oranje, Bob, Rostrup, Egill, Nielsen, Mette Ø, Osler, Merete, Fagerlund, Birgitte, Pantelis, Christos, Kinon, Bruce J, Glenthøj, Birte Y, Hansen, Lars K, Ebdrup, Bjørn H
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2020
Materias:
Poster Session I
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7234015/
http://dx.doi.org/10.1093/schbul/sbaa031.078

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7234015/
http://dx.doi.org/10.1093/schbul/sbaa031.078

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