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Using clinical information to make individualized prognostic predictions in people at ultra high risk for psychosis

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Andrea Mechelli, Ashleigh Lin, Stephen Wood, Patrick McGorry, Paul Amminger, Stefania Tognin, Philip McGuire, Jonathan Young, Barnaby Nelson, Alison Yung

Original languageEnglish
Pages (from-to)32-38
JournalSchizophrenia Research
Early online date4 Dec 2016
Publication statusPublished - Jun 2017


King's Authors


Recent studies have reported an association between psychopathology and subsequent clinical and functional outcomes in people at ultra-high risk (UHR) for psychosis. This has led to the suggestion that psychopathological information could be used to make prognostic predictions in this population. However, because the current literature is based on inferences at group level, the translational value of the findings for everyday clinical practice is unclear. Here we examined whether psychopathological information could be used to make individualized predictions about clinical and functional outcomes in people at UHR. Participants included 416 people at UHR followed prospectively at the Personal Assessment and Crisis Evaluation (PACE) Clinic in Melbourne, Australia. The data were analysed using Support Vector Machine (SVM), a supervised machine learning technique that allows inferences at the individual level. SVM predicted transition to psychosis with a specificity of 60.6%, a sensitivity of 68.6% and an accuracy of 64.6% (p .

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