Early Detection of Severe Functional Impairment Among Adolescents With Major Depression Using Logistic Classifier

Machine learning is about finding patterns and making predictions from raw data. In this study, we aimed to achieve two goals by utilizing the modern logistic regression model as a statistical tool and classifier. First, we analyzed the associations between Major Depressive Episode with Severe Impairment (MDESI) in adolescents with a list of broadly defined sociodemographic characteristics. Using findings from the logistic model, the second and ultimate goal was to identify the potential MDESI cases using a logistic model as a classifier (i.e., a predictive mechanism). Data on adolescents aged 12–17 years who participated in the National Survey on Drug Use and Health (NSDUH), 2011–2017, were pooled and analyzed. The logistic regression model revealed that compared with males and adolescents aged 12-13, females and those in the age groups of 14-15 and 16-17 had higher risk of MDESI. Blacks and Asians had lower risk of MDESI than Whites. Living in single-parent household, having less authoritative parents, having negative school experiences further increased adolescents' risk of having MDESI. The predictive model successfully identified 66% of the MDESI cases (recall rate) and accurately identified 72% of the MDESI and MDESI-free cases (accuracy rate) in the training data set. The rates of both recall and accuracy remained about the same (66 and 72%) using the test data. Results from this study confirmed that the logistic model, when used as a classifier, can identify potential cases of MDESI in adolescents with acceptable recall and reasonable accuracy rates. The algorithmic identification of adolescents at risk for depression may improve prevention and intervention

Logistic regression · Machine learning · Raw data · Recall · Statistics · Child and Adolescent Psychosocial and Emotional Development · Computer Science · Demography · Maternal Mental Health During Pregnancy and Postpartum · Mathematics · Medicine · Mental Health Research Topics · Psychology · Artificial Intelligence · Internal Medicine

• Children's Mental Health Service Use Across Service Sectors

  Barbara J Burns, E Jane Costello et al.•Health Affairs•1995

  Incluir item

• Social, Demographic, and Health Outcomes in the 10 Years Following Adolescent Depression

  Open Access•Kiyuri Naicker, Nancy L Galambos et al.•Journal of Adolescent Health•2013

  Incluir item

• Depression in adolescence

  Open Access•Anita Thapar, Stephan Collishaw et al.•The Lancet•2012

  Incluir item

• Pathways Into and Through Mental Health Services for Children and Adolescents

  Elizabeth M Z Farmer, Barbara J Burns et al.•Psychiatric Services•2003

  Incluir item

• Unsupervised Classifications of Depression Levels Based on Machine Learning Algorithms Perform Well as Compared to Traditional Norm-Based Classifications

  Open Access•Zhenkai Yang, C Chen et al.•Frontiers in Psychiatry•2020

  Incluir item

• Adolescent Depression

  Wenhua Lu•American Journal of Health Behavior•2018

  Incluir item