Clinical and psychological factors associated with resilience in patients with schizophrenia: data from the Italian network for research on psychoses using machine learning.
Italian network for research on psychoses
machine learning
personalized interventions
resilience
schizophrenia
Journal
Psychological medicine
ISSN: 1469-8978
Titre abrégé: Psychol Med
Pays: England
ID NLM: 1254142
Informations de publication
Date de publication:
09 2023
09 2023
Historique:
medline:
23
10
2023
pubmed:
12
10
2022
entrez:
11
10
2022
Statut:
ppublish
Résumé
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). SCZ from the Italian Network for Research on Psychoses ( The algorithm classified patients as HR or LR with a Balanced Accuracy of 74.5% ( We identified an accurate, meaningful and generalizable clinical-psychological signature associated with resilience in SCZ. This study delivers relevant information regarding psychological and clinical factors that non-pharmacological interventions could target in schizophrenia.
Sections du résumé
BACKGROUND
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).
METHODS
SCZ from the Italian Network for Research on Psychoses (
RESULTS
The algorithm classified patients as HR or LR with a Balanced Accuracy of 74.5% (
CONCLUSIONS
We identified an accurate, meaningful and generalizable clinical-psychological signature associated with resilience in SCZ. This study delivers relevant information regarding psychological and clinical factors that non-pharmacological interventions could target in schizophrenia.
Identifiants
pubmed: 36217912
doi: 10.1017/S003329172200294X
pii: S003329172200294X
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM