Predicting the individual risk of psychosis conversion in at-risk mental state (ARMS): a multivariat...
Predicting the individual risk of psychosis conversion in at-risk mental state (ARMS): a multivariate model reveals the influence of nonpsychotic prodromal symptoms
About this item
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Author / Creator
Publisher
Berlin/Heidelberg: Springer Berlin Heidelberg
Journal title
Language
English
Formats
Publication information
Publisher
Berlin/Heidelberg: Springer Berlin Heidelberg
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More information
Scope and Contents
Contents
To improve the prediction of the individual risk of conversion to psychosis in UHR subjects, by considering all CAARMS’ symptoms at first presentation and using a multivariate machine learning method known as logistic regression with Elastic-net shrinkage. 46 young individuals who sought help from the specialized outpatient unit at Sainte-Anne hosp...
Alternative Titles
Full title
Predicting the individual risk of psychosis conversion in at-risk mental state (ARMS): a multivariate model reveals the influence of nonpsychotic prodromal symptoms
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Record Identifier
TN_cdi_proquest_miscellaneous_2330341387
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_miscellaneous_2330341387
Other Identifiers
ISSN
1018-8827
E-ISSN
1435-165X
DOI
10.1007/s00787-019-01461-y
