Development and validation of multivariable prediction models of remission, recovery, and quality of life outcomes in people with first episode psychosis: a machine learning approach

Development and validation of multivariable prediction models of remission, recovery, and quality of life outcomes in people with first episode psychosis: a machine learning approach

Summary: Background: Outcomes for people with first-episode psychosis are highly heterogeneous. Few reliable validated methods are available to predict the outcome for individual patients in the first clinical contact. In this study, we aimed to build multivariable prediction models of 1-year remis...

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Main Authors: Samuel P Leighton, MBChB, Rachel Upthegrove, ProfPhD, Rajeev Krishnadas, PhD, Michael E Benros, PhD, Matthew R Broome, ProfPhD, Georgios V Gkoutos, ProfPhD, Peter F Liddle, ProfPhD, Swaran P Singh, ProfMD, Linda Everard, BSc, Peter B Jones, ProfMD, David Fowler, ProfMSc, Vimal Sharma, ProfPhD, Nicholas Freemantle, ProfPhD, Rune H B Christensen, PhD, Nikolai Albert, PhD, Merete Nordentoft, ProfPhD, Matthias Schwannauer, ProfPhD, Jonathan Cavanagh, ProfMD, Andrew I Gumley, ProfPhD, Max Birchwood, ProfPhD, Pavan K Mallikarjun, PhD
Format: Article
Language:English
Published: Elsevier 2019-10-01
Series:The Lancet: Digital Health
Online Access:http://www.sciencedirect.com/science/article/pii/S2589750019301219

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http://www.sciencedirect.com/science/article/pii/S2589750019301219

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