A machine-learning framework for robust and reliable prediction of short- and long-term treatment response in initially antipsychotic-naïve schizophrenia patients based on multimodal neuropsychiatric data

The reproducibility of machine-learning analyses in computational psychiatry is a growing concern. In a multimodal neuropsychiatric dataset of antipsychotic-naïve, first-episode schizophrenia patients, we discuss a workflow aimed at reducing bias and overfitting by invoking simulated data in the des...

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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: Nature Publishing Group UK 2020
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7417553/
https://www.ncbi.nlm.nih.gov/pubmed/32778656
http://dx.doi.org/10.1038/s41398-020-00962-8

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7417553/
https://www.ncbi.nlm.nih.gov/pubmed/32778656
http://dx.doi.org/10.1038/s41398-020-00962-8

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