A machine-learning framework for robust and reliable prediction of short- and long-term treatment re...
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
About this item
Full title
Author / Creator
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. and Ebdrup, Bjørn H.
Publisher
London: Nature Publishing Group UK
Journal title
Language
English
Formats
Publication information
Publisher
London: Nature Publishing Group UK
Subjects
More information
Scope and Contents
Contents
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 design process and analysis in two independent machin...
Alternative Titles
Full title
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
Authors, Artists and Contributors
Author / Creator
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.
Identifiers
Primary Identifiers
Record Identifier
TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_7417553
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_7417553
Other Identifiers
ISSN
2158-3188
E-ISSN
2158-3188
DOI
10.1038/s41398-020-00962-8
