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Using combined environmental-clinical classification models to predict role functioning outcome in clinical high-risk states for psychosis and recent-onset depression.

Linda A AntonucciNora PenzelRachele SanfeliciAlessandro PigoniLana Kambeitz-IlankovicDominic DwyerAnne RuefMark Sen DongÖmer Faruk ÖztürkKatharine ChisholmTheresa K HaidlMarlene RosenAdele FerroGiulio PergolaIleana AndriolaGiuseppe BlasiStephan RuhrmannFrauke Schultze-LutterPeter FalkaiJoseph KambeitzRebekka LencerUdo DannlowskiRachel UpthegroveRaimo K R SalokangasChristos PantelisEva MeisenzahlStephen J WoodPaolo BrambillaStefan BorgwardtAlessandro BertolinoNikolaos Koutsoulerisnull null
Published in: The British journal of psychiatry : the journal of mental science (2022)
Findings support the hypothesis that environmental variables inform role outcome prediction, highlight the existence of both transdiagnostic and syndrome-specific predictive environmental adverse events, and emphasise the importance of implementing real-world models by measuring multiple risk dimensions.
Keyphrases
  • human health
  • life cycle
  • machine learning
  • deep learning
  • depressive symptoms
  • risk assessment
  • sleep quality