Systematic misestimation of machine learning performance in neuroimaging studies of depression

We currently observe a disconcerting phenomenon in machine learning studies in psychiatry: While we would expect larger samples to yield better results due to the availability of more data, larger machine learning studies consistently show much weaker performance than the numerous small-scale studie...

Descripción completa

Detalles Bibliográficos
Autores principales: Flint, Claas, Cearns, Micah, Opel, Nils, Redlich, Ronny, Mehler, David M. A., Emden, Daniel, Winter, Nils R., Leenings, Ramona, Eickhoff, Simon B., Kircher, Tilo, Krug, Axel, Nenadic, Igor, Arolt, Volker, Clark, Scott, Baune, Bernhard T., Jiang, Xiaoyi, Dannlowski, Udo, Hahn, Tim
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8209109/
https://www.ncbi.nlm.nih.gov/pubmed/33958703
http://dx.doi.org/10.1038/s41386-021-01020-7

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8209109/
https://www.ncbi.nlm.nih.gov/pubmed/33958703
http://dx.doi.org/10.1038/s41386-021-01020-7

Ejemplares similares