Skip to main content

Advertisement

SpringerLink
Log in

Leveraging Machine Learning to Identify Predictors of Receiving Psychosocial Treatment for Attention Deficit/Hyperactivity Disorder

  • Original Paper
  • Published:
Administration and Policy in Mental Health and Mental Health Services Research Aims and scope Submit manuscript

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Similar content being viewed by others

References

  • Adamson, D. M., Chang, S., & Hansen, L. G. (2008). Health research data for the real world: The MarketScan databases (p. b28). New York: Thompson Healthcare.

    Google Scholar 

  • Aguiar, E., Lakkaraju, H., Bhanpuri, N., Miller, D., Yuhas, B., & Addison, K. L. (2015). Who, when, and why: A machine learning approach to prioritizing students at risk of not graduating high school on time. In Proceedings of the Fifth International Conference on Learning Analytics And Knowledge (pp. 93–102).https://doi.org/10.1145/2723576.2723619

  • American Academy of Pediatrics: Subcommittee on ADHD, steering committee of Quality Improvement and Management (2011). ADHD: Clinical Practice Guideline for the Diagnosis, Evaluation, and Treatment of Attention-Deficit/Hyperactivity Disorder in Children and Adolescents. Pediatrics, 128(5), 1007–1022. https://doi.org/10.1542/peds.2011-2654.

  • Altszuler, A. R., Page, T. F., Gnagy, E. M., Coxe, S., Arrieta, A., Molina, B. S. G., et al. (2015). Financial dependence of young adults with childhood ADHD. Journal of Abnormal Child Psychology. https://doi.org/10.1007/s10802-015-0093-9.

    Article  Google Scholar 

  • Anastopoulos, A. D. (2001). Assessing attention-deficit/hyperactivity disorder (2001st ed.). New York: Springer.

    Google Scholar 

  • Aulck, L., Velagapudi, N., Blumenstock, J., & West, J. (2016). Predicting student dropout in higher education. Retrieved from https://arxiv.org/abs/1606.06364v4.

  • Auret, L., & Aldrich, C. (2011). Empirical comparison of tree ensemble variable importance measures. Chemometrics and Intelligent Laboratory Systems,105(2), 157–170. https://doi.org/10.1016/j.chemolab.2010.12.004.

    Article  CAS  Google Scholar 

  • Bandura, A., & McClelland, D. C. (1977). Social learning theory. Retrieved from https://www.esludwig.com/uploads/2/6/1/0/26105457/bandura_sociallearningtheory.pdf.

  • Bezanson, J., Karpinski, S., Shah, V. B., & Edelman, A. (2012). Julia: A fast dynamic language for technical computing. ArXiv:1209.5145 [Cs]. Retrieved from https://arxiv.org/abs/1209.5145

  • BigML. (2018). Retrieved from https://bigml.com. BigML, Inc.: Corvallis, OR.

  • Bowers, A. J., & Zhou, X. (2019). Receiver operating characteristic (ROC) area under the curve (AUC): A diagnostic measure for evaluating the accuracy of predictors of education outcomes. Journal of Education for Students Placed at Risk,24(1), 20–46. https://doi.org/10.1080/10824669.2018.1523734.

    Article  Google Scholar 

  • Breiman, L. (2001). Statistical modeling: The two cultures (with comments and a rejoinder by the author). Statistical Science,16(3), 199–231. https://doi.org/10.1214/ss/1009213726.

    Article  Google Scholar 

  • Breiman, L. (2017). Classification and regression trees. Routledge. https://doi.org/10.1201/9781315139470.

    Article  Google Scholar 

  • Burke, T. A., Ammerman, B. A., & Jacobucci, R. (2019). The use of machine learning in the study of suicidal and non-suicidal self-injurious thoughts and behaviors: A systematic review. Journal of Affective Disorders,245, 869–884. https://doi.org/10.1016/j.jad.2018.11.073.

    Article  PubMed  Google Scholar 

  • Campbell, D. T., & Fiske, D. W. (1959). Convergent and discriminant validation by multitrait-multimethod matrix. Psychological Bulletin,56, 81–105.

    Article  CAS  PubMed  Google Scholar 

  • Caye, A., Agnew-Blais, J., Arseneault, L., Gonçalves, H., Kieling, C., Langley, K., et al. (2019). A risk calculator to predict adult attention-deficit/hyperactivity disorder: Generation and external validation in three birth cohorts and one clinical sample. Epidemiology and Psychiatric Sciences,29, 1–9.

    Google Scholar 

  • Child and Adolescent Health Measurement Initiative. (2017). 2016 National Survey of Children’s Health (2017), Sampling and Survey Administration. Data Resource Center, supported by Cooperative Agreement 1‐U59‐MC06980‐01 from the U.S. Department of Health and Human Services, Health Resources and Services Administration (HRSA), Maternal and Child Health Bureau (MCHB). Retrieved from www.childhealthdata.org. Revised 04/26/17.

  • Child and Adolescent Health Measurement Initiative. (2018). Child and Family Health Measures Content Map, 2017 National Survey of Children’s Health. Data Resource Center for Child and Adolescent Health supported by Cooperative Agreement U59MC27866 from the U.S. Department of Health and Human Services, Health Resources and Services Administration’s Maternal and Child Health Bureau (HRSA MCHB). Retrieved from www.childhealthdata.org, Revised 9/26/2018

  • Child and Adolescent Health Measurement Initiative (CAHMI). (2019). 2016–2017 National Survey of Children’s Health, SPSS Indicator Data Set. Data Resource Center for Child and Adolescent Health supported by Cooperative Agreement from the U.S. Department of Health and Human Services, Health Resources and Services Administration (HRSA), Maternal and Child Health Bureau (MCHB). Retrieved 15 April, 2019, from www.childhealthdata.org.

  • Cummins, R., Ewbank, M. P., Martin, A., Tablan, V., Catarino, A., & Blackwell, A. D. (2019). TIM: A tool for gaining insights into psychotherapy. The World Wide Web Conference. https://doi.org/10.1145/3308558.3314128.

    Article  Google Scholar 

  • Danielson, M. L., Bitsko, R. H., Ghandour, R. M., Holbrook, J. R., Kogan, M. D., & Blumberg, S. J. (2018). Prevalence of parent-reported ADHD diagnosis and associated treatment among US children and adolescents, 2016. Journal of Clinical Child and Adolescent Psychology,47(2), 199–212. https://doi.org/10.1080/15374416.2017.1417860.

    Article  PubMed  PubMed Central  Google Scholar 

  • DuPaul, G. J., Chronis-Tuscano, A., Danielson, M. L., & Visser, S. N. (2018). Predictors of receipt of school services in a national sample of youth with ADHD. Journal of Attention Disorders. https://doi.org/10.1177/1087054718816169.

    Article  PubMed  PubMed Central  Google Scholar 

  • Eiraldi, R. B., Mazzuca, L. B., Clarke, A. T., & Power, T. J. (2006). Service utilization among ethnic minority children with ADHD: A model of help-seeking behavior. Administration and Policy in Mental Health and Mental Health Services Research,33(5), 607–622. https://doi.org/10.1007/s10488-006-0063-1.

    Article  PubMed  Google Scholar 

  • Evans, S. W., Owens, J. S., Wymbs, B. T., & Ray, A. R. (2018). Evidence-based psychosocial treatments for children and adolescents with Attention Deficit/Hyperactivity Disorder. Journal of Clinical Child & Adolescent Psychology,47(2), 157–198. https://doi.org/10.1080/15374416.2017.1390757.

    Article  Google Scholar 

  • Fitzpatrick, K. K., Darcy, A., & Vierhile, M. (2017). Delivering cognitive behavior therapy to young adults with symptoms of depression and anxiety using a fully automated conversational agent (Woebot): A randomized controlled trial. JMIR Mental Health,4(2), e19. https://doi.org/10.2196/mental.7785.

    Article  PubMed  PubMed Central  Google Scholar 

  • Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. The Annals of Statistics,29(5), 1189–1232. https://doi.org/10.1214/aos/1013203451.

    Article  Google Scholar 

  • Gomez-Uribe, C. A., & Hunt, N. (2015). The netflix recommender system: Algorithms, business value, and innovation. ACM Transactions on Management Information Systems,6(4), 13–19. https://doi.org/10.1145/2843948.

    Article  Google Scholar 

  • Hammond, E. C., & Horn, D. (1954). The relationship between human smoking habits and death rates: A follow-up study of 187,766 men. Journal of the American Medical Association,155(15), 1316–1328. https://doi.org/10.1001/jama.1954.03690330020006.

    Article  CAS  PubMed  Google Scholar 

  • Harrison, R., Samaraweera, L. G., Dobie, M. R., & Lewis, P. H. (1996). Comparing programming paradigms: An evaluation of functional and object-oriented programs. Software Engineering Journal,11(4), 247–254.

    Article  Google Scholar 

  • IBM Corp. (2019). IBM SPSS statistics for windows, version 26.0. Armonk, NY: IBM Corp.

    Google Scholar 

  • Huber, B., Davis III, R. F., Cotter, A., Junkin, E., Yard, M., Shieber, S., Brestan-Knight, E., & Gajos, K. Z. (2019). SpecialTime: Automatically detecting dialogue acts from speech to support parent-child interaction therapy. Proceedings of the 13th EAI International Conference on Pervasive Computing Technologies for Healthcare, 139–148.

  • Jensen, P. S., Hinshaw, S. P., Swanson, J. M., Greenhill, L. L., Conners, C. K., Arnold, L. E., et al. (2001). Findings from the NIMH multimodal treatment study of ADHD (MTA): Implications and applications for primary care providers. Journal of Developmental & Behavioral Pediatrics,22(1), 60.

    Article  CAS  Google Scholar 

  • Kapczinski, F., Passos, I. C., & Mwangi, B. (2019). Personalized psychiatry: Big data analytics in mental health. New York: Springer.

    Google Scholar 

  • Kass, G. V. (1980). An exploratory technique for investigating large quantities of categorical data. Journal of the Royal Statistical Society: Series C (Applied Statistics),29(2), 119–127. https://doi.org/10.2307/2986296.

    Article  Google Scholar 

  • Kessler, R. C., Bernecker, S. L., Bossarte, R. M., Luedtke, A. R., McCarthy, J. F., Nock, M. K., et al. (2019). The role of big data analytics in predicting suicide. In I. C. Passos, B. Mwangi, & F. Kapczinski (Eds.), Personalized psychiatry: Big data analytics in mental health (pp. 77–98). New York: Springer. https://doi.org/10.1007/978-3-030-03553-2_5.

    Chapter  Google Scholar 

  • Kim, J.-W., Sharma, V., & Ryan, N. D. (2015). Predicting methylphenidate response in ADHD using machine learning approaches. The International Journal of Neuropsychopharmacology,18(11), pyv052. https://doi.org/10.1093/ijnp/pyv052.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Knowles, J. E. (2015). Of needles and haystacks: Building an accurate statewide dropout early warning system in Wisconsin. Journal of Educational Data Mining,7(3), 18–67.

    Google Scholar 

  • Konig, I. R., Malley, J. D., Weimar, C., Diener, H. C., Ziegler, A., & German Stroke Study, C. (2007). Practical experiences on the necessity of external validation. Statistics in Medicine,26, 5499–5511.

    Article  CAS  PubMed  Google Scholar 

  • Kuriyan, A. B., Pelham, W. E., Molina, B. S. G., Waschbusch, D. A., Gnagy, E. M., Sibley, M. H., et al. (2013). Young adult educational and vocational outcomes of children diagnosed with ADHD. Journal of Abnormal Child Psychology,41(1), 27–41. https://doi.org/10.1007/s10802-012-9658-z.

    Article  PubMed  PubMed Central  Google Scholar 

  • Lenhard, F., Sauer, S., Andersson, E., Månsson, K. N., Mataix-Cols, D., Rück, C., et al. (2018). Prediction of outcome in internet-delivered cognitive behaviour therapy for paediatric obsessive-compulsive disorder: A machine learning approach. International Journal of Methods in Psychiatric Research,27(1), e1576. https://doi.org/10.1002/mpr.1576.

    Article  Google Scholar 

  • Merrill, B. M., Molina, B. S. G., Coxe, S., Gnagy, E. M., Altszuler, A. R., Macphee, F. L., et al. (2019). Functional outcomes of young adults with childhood ADHD: A latent profile analysis. Journal of Clinical Child and Adolescent Psychology: The Official Journal for the Society of Clinical Child and Adolescent Psychology, American Psychological Association, Division,53, 1–14. https://doi.org/10.1080/15374416.2018.1547968.

    Article  Google Scholar 

  • Moons, K. G., Altman, D. G., Reitsma, J. B., Ioannidis, J. P., Macaskill, P., Steyerberg, E. W., et al. (2015). Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): Explanation and elaboration. Annals of Internal Medicine,162(1), W1–W73.

    Article  PubMed  Google Scholar 

  • Murray, D. W., Molina, B. S. G., Glew, K., Houck, P., Greiner, A., Fong, D., et al. (2014). Prevalence and characteristics of school services for high school students with Attention-Deficit/Hyperactivity Disorder. School Mental Health,6(4), 264–278. https://doi.org/10.1007/s12310-014-9128-6.

    Article  PubMed  PubMed Central  Google Scholar 

  • Ng, X., Bridges, J. F. P., Ross, M. M., Frosch, E., Reeves, G., Cunningham, C. E., et al. (2017). A latent class analysis to identify variation in caregivers’ preferences for their child’s Attention-Deficit/Hyperactivity Disorder treatment: Do stated preferences match current treatment? The Patient,10(2), 251–262. https://doi.org/10.1007/s40271-016-0202-z.

    Article  PubMed  PubMed Central  Google Scholar 

  • Office of Inspector General (OIG). (2019). Many Medicaid-enrolled children who were treated for ADHD did not receive recommended follow-up care. U.S. Department of Health and Human Services: Washington, DC. Retrieved from oig.hhs.gov/oei/reports/oei-07-17-00170.asp.

  • Page, T. F., Pelham, W. E., Fabiano, G. A., Greiner, A. R., Gnagy, E. M., Hart, K. C., et al. (2016). Comparative cost analysis of sequential, adaptive, behavioral, pharmacological, and combined treatments for childhood ADHD. Journal of Clinical Child and Adolescent Psychology, Division,45(4), 416–427. https://doi.org/10.1080/15374416.2015.1055859.

    Article  Google Scholar 

  • Pelham, W. E. P., Jr., Fabiano, G. A., Waxmonsky, J. G., Greiner, A. R., Gnagy, E. M., III, Pelham, W. E., et al. (2016). Treatment sequencing for childhood ADHD: A multiple-randomization study of adaptive medication and behavioral interventions. Journal of Clinical Child & Adolescent Psychology,45(4), 396–415. https://doi.org/10.1080/15374416.2015.1105138.

    Article  Google Scholar 

  • Pérez-Rosas, V., Mihalcea, R., Resnicow, K., Singh, S., & An, L. (2016). Building a motivational interviewing dataset. In Proceedings of the Third Workshop on Computational Linguistics and Clinical Psychology (pp. 42–51). https://doi.org/10.18653/v1/W16-0305

  • Pérez-Rosas, V., Mihalcea, R., Resnicow, K., Singh, S., Ann, L., Goggin, K. J., et al. (2017). Predicting counselor behaviors in motivational interviewing encounters. Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers, pp. 1128–1137. Retrieved from https://aclweb.org/anthology/E17-1106

  • Podsakoff, P. M., MacKenzie, S. B., & Podsakoff, N. P. (2012). Sources of method bias in social science research and recommendations on how to control it. Annual Review of Psychology,63, 539–569.

    Article  PubMed  Google Scholar 

  • Samuel, A. L. (1959). Some studies in machine learning using the game of checkers. IBM Journal of Research and Development,3(3), 210–229.

    Article  Google Scholar 

  • Sansone, D. (2019). Beyond early warning indicators: High school dropout and machine learning. Oxford Bulletin of Economics and Statistics,81(2), 456–485. https://doi.org/10.1111/obes.12277.

    Article  Google Scholar 

  • Sara, N.-B., Halland, R., Igel, C., & Alstrup, S. (2015). High-school dropout prediction using machine learning: A Danish large-scale study. European Symposium on Artificial Neural Networks.

  • Schatz, N., Fabiano, G., Cunningham, C., dosReis, S., Waschbusch, D., Jerome, S., et al. (2015). Systematic review of patients’ and parents’ preferences for ADHD treatment options and processes of care. The Patient: Patient-Centered Outcomes Research,8(6), 483–497.

    Article  Google Scholar 

  • Sibley, M. H., Pelham, W. E., Molina, B. S. G., Gnagy, E. M., Waschbusch, D. A., Garefino, A. C., et al. (2012). Diagnosing ADHD in adolescence. Journal of Consulting and Clinical Psychology,80(1), 139–150. https://doi.org/10.1037/a0026577.

    Article  PubMed  Google Scholar 

  • Subcommittee on Attention-Deficit, Hyperactivity Disorder, S. C. on Q. I. & M. (2011). ADHD: Clinical practice guideline for the diagnosis, evaluation, and treatment of Attention-Deficit/Hyperactivity Disorder in children and adolescents. Pediatrics. https://doi.org/10.1542/peds.2011-2654.

    Article  Google Scholar 

  • Vincent, P., Larochelle, H., Bengio, Y., & Manzagol, P.-A. (2008). Extracting and composing robust features with denoising autoencoders. In Proceedings of the 25th International Conference on Machine Learning (pp. 1096–1103). New York, NY: ACM. https://doi.org/10.1145/1390156.1390294

  • Waschbusch, D. A., Cunningham, C. E., Pelham, W. E., Rimas, H. L., Greiner, A. R., Gnagy, E. M., et al. (2011). A discrete choice conjoint experiment to evaluate parent preferences for treatment of young, medication naive children with ADHD. Journal of Clinical Child and Adolescent Psychology The Official Journal for the Society of Clinical Child and Adolescent Psychology, American Psychological Association, Division,40(4), 546–561. https://doi.org/10.1080/15374416.2011.581617.

    Article  Google Scholar 

  • Waxmonsky, J. G., Baweja, R., Liu, G., Waschbusch, D. A., Fogel, B., Leslie, D., et al. (2019). A commercial insurance claims analysis of correlates of behavioral therapy use among Children With ADHD. Psychiatric Services. https://doi.org/10.1176/appi.ps.201800473.

    Article  PubMed  Google Scholar 

  • Youngstrom, E. A. (2014). A primer on receiver operating characteristic analysis and diagnostic efficiency statistics for pediatric psychology: We are ready to ROC. Journal of Pediatric Psychology,39(2), 204–221. https://doi.org/10.1093/jpepsy/jst062.

    Article  PubMed  Google Scholar 

  • Youngstrom, E. A., Halverson, T. F., Youngstrom, J. K., Lindhiem, O., & Findling, R. L. (2018). Evidence-based assessment From simple clinical judgments to statistical learning: Evaluating a range of options using pediatric bipolar disorder as a diagnostic challenge. Clinical Psychological Science,6(2), 243–265. https://doi.org/10.1177/2167702617741845.

    Article  PubMed  Google Scholar 

  • Zablotsky, B., Bramlett, M. D., Visser, S. N., Danielson, M. L., & Blumberg, S. J. (2018). Latent class analysis of ADHD neurodevelopmental and mental health comorbidities. Journal of Developmental and Behavioral Pediatrics,39(1), 10–19. https://doi.org/10.1097/DBP.0000000000000508.

    Article  PubMed  PubMed Central  Google Scholar 

  • Zendarski, N., Sciberras, E., Mensah, F., & Hiscock, H. (2018). Factors associated with educational support in young adolescents with ADHD. Journal of Attention Disorders. https://doi.org/10.1177/1087054718804351.

    Article  PubMed  Google Scholar 

  • Zhao, X., Page, T. F., Altszuler, A. R., Pelham, W. E., Kipp, H., Gnagy, E. M., et al. (2019). Family burden of raising a child with ADHD. Journal of Abnormal Child Psychology. https://doi.org/10.1007/s10802-019-00518-5.

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

This work was completed while Dr. Morrow was at FIU. She is currently a post-doctoral fellow at Nova Southeastern University.

Author information

Authors and Affiliations

  1. Department of Psychology, Florida International University, Miami, USA

    Anne S. Morrow, Xin Zhao & Michelle M. Liriano

  2. Center for Children and Families, Florida International University, Miami, USA

    Anne S. Morrow & Xin Zhao

  3. College of Psychology, Nova Southeastern University, Davie, USA

    Anne S. Morrow

  4. Zyanya Tech, LLC, Miami, USA

    Alexandro D. Campos Vega

Authors
  1. Anne S. Morrow
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Alexandro D. Campos Vega
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xin Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Michelle M. Liriano
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Anne S. Morrow.

Ethics declarations

Conflict of interest

The authors do not have any conflicts of interest to disclose.

Ethical Approval

The following study used publicly available data from the National Survey of Children’s Health (CAHMI, 2019). All procedures performed in the NSCH study involving human participants were in accordance with the ethical standards of the IRB HHS regulations (45CFR 46), these procedures are reviewed by the NHS Research Ethics Review Board (ERB) and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Research Involving Human Rights

The current study was determined to be exempt from institutional review board review at Florida International University, not categorized as human subjects research because the data were completely de-identified at the time of study.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Morrow, A.S., Campos Vega, A.D., Zhao, X. et al. Leveraging Machine Learning to Identify Predictors of Receiving Psychosocial Treatment for Attention Deficit/Hyperactivity Disorder. Adm Policy Ment Health 47, 680–692 (2020). https://doi.org/10.1007/s10488-020-01045-y

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10488-020-01045-y

Keywords

Search

Navigation