Leveraging Machine Learning to Identify Predictors of Receiving Psychosocial Treatment for Attention Deficit/Hyperactivity Disorder
| Autor: | Michelle M. Liriano, Xin Zhao, Anne S. Morrow, Alexandro D. Campos Vega |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
050103 clinical psychology
medicine.medical_specialty business.industry Health Policy Public health 05 social sciences Public Health Environmental and Occupational Health medicine.disease Logistic regression Machine learning computer.software_genre Affect (psychology) Mental health Health informatics Health administration Psychiatry and Mental health medicine Attention deficit hyperactivity disorder 0501 psychology and cognitive sciences Artificial intelligence Pshychiatric Mental Health business Psychosocial computer 050104 developmental & child psychology |
| Zdroj: | Administration and Policy in Mental Health and Mental Health Services Research. 47:680-692 |
| ISSN: | 1573-3289 0894-587X |
| DOI: | 10.1007/s10488-020-01045-y |
| Popis: | This study aimed to identify factors associated with receiving psychosocial treatment for ADHD in a nationally representative sample. Participants were 6630 youth with a parent-reported diagnosis of ADHD from the 2016–2017 National Survey of Children’s Health. Machine learning analyses were performed to identify factors associated with receipt of psychosocial treatment for ADHD. We examined potentially associated factors in the broad categories of variables hypothesized to affect problem recognition (e.g., severity, mental health comorbidities); the decision to seek treatment; service selection (e.g., insurance coverage) and service use. We found that three machine learning models unanimously identified parent-reported ADHD severity (mild vs. moderate/severe) as the factor that best distinguishes between children who receive psychosocial treatment for ADHD and those who do not. Receive operating characteristic curve analysis revealed the following model performance: classification and regression tree analysis (area under the curve; AUC = .68); an ensemble model (AUC = .71); and a deep, multi-layer neural network (AUC = .72), as well as comparison to a logistic regression model (AUC = .69). Further, insurance coverage of mental/behavioral health needs emerged as a salient factor associated with the receipt of psychosocial treatment. Machine learning models identified risk and protective factors that predicted the receipt of psychosocial treatment for ADHD, such as ADHD severity and health insurance coverage. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full text from SpringerLink