Resilience is defined as the ability to modify thoughts to cope with stressful events. Patients with schizophrenia (SCZ) having higher resilience (HR) levels show less severe symptoms and better real-life functioning. However, the clinical factors contributing to determine resilience levels in patients remain unclear. Thus, based on psychological, historical, clinical and environmental variables, we built a supervised machine learning algorithm to classify patients with HR or lower resilience (LR).

Clinical and psychological factors associated with resilience in patients with schizophrenia: data from the Italian network for research on psychoses using machine learning

Rocca, Paola;Brasso, Claudio;
2022-01-01

Abstract

Resilience is defined as the ability to modify thoughts to cope with stressful events. Patients with schizophrenia (SCZ) having higher resilience (HR) levels show less severe symptoms and better real-life functioning. However, the clinical factors contributing to determine resilience levels in patients remain unclear. Thus, based on psychological, historical, clinical and environmental variables, we built a supervised machine learning algorithm to classify patients with HR or lower resilience (LR).
2022
1
12
Italian network for research on psychoses; machine learning; personalized interventions; resilience; schizophrenia
Antonucci, Linda A; Pergola, Giulio; Rampino, Antonio; Rocca, Paola; Rossi, Alessandro; Amore, Mario; Aguglia, Eugenio; Bellomo, Antonello; Bianchini, Valeria; Brasso, Claudio; Bucci, Paola; Carpiniello, Bernardo; Dell'Osso, Liliana; di Fabio, Fabio; di Giannantonio, Massimo; Fagiolini, Andrea; Giordano, Giulia Maria; Marcatilli, Matteo; Marchesi, Carlo; Meneguzzo, Paolo; Monteleone, Palmiero; Pompili, Maurizio; Rossi, Rodolfo; Siracusano, Alberto; Vita, Antonio; Zeppegno, Patrizia; Galderisi, Silvana; Bertolino, Alessandro; Maj, Mario
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/1876489
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