Classification of psychiatric symptoms using deep interaction networks: the CASPIAN-IV study
| Autor: | Harald Binder, Mohammad Esmaiel Motlagh, Marjan Mansourian, Moritz Hess, Zahra Tasdighi, Azam Naghavi, Mohammad Reza Mohebian, Miguel Angel Mañanas, Roya Kelishadi, Ramin Heshmat, Hamid Reza Marateb |
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| Přispěvatelé: | Universitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial, Universitat Politècnica de Catalunya. BIOART - BIOsignal Analysis for Rehabilitation and Therapy |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Trastorns mentals -- Classificació
Economic growth Adolescent Science Iran Violence Paediatric research Article Factors de risc (Medicina) Informàtica [Àrees temàtiques de la UPC] Surveys and Questionnaires Political science Agency (sociology) Humans Psychology media_common.cataloged_instance Obesity Psiquiatria European union Child Students Exercise Life Style media_common Government Multidisciplinary Mental Disorders Ciències de la salut::Salut mental::Psiquiatria [Àrees temàtiques de la UPC] Feeding Behavior Classification Team working Classificació Cross-Sectional Studies ROC Curve Risk factors Psychosocial issues Medicine Psychiatric disorders Biomedical engineering |
| Zdroj: | UPCommons. Portal del coneixement obert de la UPC Universitat Politècnica de Catalunya (UPC) Scientific Reports, Vol 11, Iss 1, Pp 1-15 (2021) Scientific Reports |
| DOI: | 10.1038/s41598-021-95208-y |
| Popis: | Identifying the possible factors of psychiatric symptoms among children can reduce the risk of adverse psychosocial outcomes in adulthood. We designed a classification tool to examine the association between modifiable risk factors and psychiatric symptoms, defined based on the Persian version of the WHO-GSHS questionnaire in a developing country. Ten thousand three hundred fifty students, aged 6–18 years from all Iran provinces, participated in this study. We used feature discretization and encoding, stability selection, and regularized group method of data handling (GMDH) to classify the a priori specific factors (e.g., demographic, sleeping-time, life satisfaction, and birth-weight) to psychiatric symptoms. Self-rated health was the most critical feature. The selected modifiable factors were eating breakfast, screentime, salty snack for depression symptom, physical activity, salty snack for worriedness symptom, (abdominal) obesity, sweetened beverage, and sleep-hour for mild-to-moderate emotional symptoms. The area under the ROC curve of the GMDH was 0.75 (CI 95% 0.73–0.76) for the analyzed psychiatric symptoms using threefold cross-validation. It significantly outperformed the state-of-the-art (adjusted p¿ |
| Databáze: | OpenAIRE |
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