Novel Neural Network Models for Predicting Mental Health Outcomes in the U.S. Youth Population (2024)

Authors

Avi VermaPalo Alto High School, Palo Alto, CA
Kaustubh SupekarDepartment of Psychiatry and Behavioral Sciences, Stanford Medicine, Stanford University, Stanford, CA

DOI:

https://doi.org/10.47611/jsrhs.v13i1.5941

Keywords:

Mental Health, Neural Network, Demographic Background

Abstract

Anxiety and depression are two of the most pressing mental health issues, particularly among young adults. Given the success of neural networks for predictive modeling, we developed novel neural network models for classifying anxiety and depression using Substance Abuse and Mental Health Services Administration’s Mental Health Client- Level Data (SAMHSA MH-CLD) on 382,174 young adults (15 to 24 years old). The SAMHSA MH-CLD included mental health and general background data collected in 2020 for individuals reporting to state-accredited hospital service centers across the United States. The neural networks were trained on 10% randomized k-folds of the dataset and tested on the remaining 90% for each fold. We found that neural network models predicted anxiety and depression with high accuracy (91.5% to 94.2% accuracy, 8.4% to 3.1% loss), outperforming conventional statistical models. Additionally, for all tested variable sets, our neural network model outperformed expectations for the average therapist consultation (46% to 50% accuracy), as reported previously. For the optimal neural network model with the highest accuracy, the variables most correlated with anxiety and depression were age, education, gender, race, employment, marital status, and stressor events, not accounting for redundant and minimally correlated variables. The effectiveness of our neural network model indicates that it can be implemented alongside therapists in clinical environments to improve psychiatric diagnosis among young adults.

Downloads

Download data is not yet available.

Author Biography

Kaustubh Supekar, Department of Psychiatry and Behavioral Sciences, Stanford Medicine, Stanford University, Stanford, CA

Assocaiate Professor, Department of Psychiatry and Behavioral Sciences

References or Bibliography

Afifi, M. (2007). Gender differences in mental health. Singapore Medical Journal, 48(5), 385-391. https://pubmed.ncbi.nlm.nih.gov/17453094/

Al-Huthail, Y. R. (2008). Accuracy of referring psychiatric diagnosis. International Journal of Health Science, 2(1), 35-38. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3068718/

Araya, R. A. (2003). Education and income: Which is more important for mental health? Journal of Epidemiology & Community Health, 57(7), 501-505. https://doi.org/10.1136/jech.57.7.501

Barron, J. (2023, January 22). Therapy session rates by CPT code. SimplePractice. Retrieved August 20, 2023, from https://www.simplepractice.com/blog/top-billed-cpt-codes/

Businelle, M. S., Mills, B. A., Chartier, K. G., Kendzor, D. E., Reingle, J. M., & Shuval, K. (2013). Do stressful events account for the link between socioeconomic status and mental health? Journal of Public Health, 36(2), 205-212. https://doi.org/10.1093/pubmed/fdt060

Centers for Disease Control and Prevention. (2021, May 12). Mental Health. Centers for Disease Control and Prevention. Retrieved September 5, 2022, from https://www.cdc.gov/healthyyouth/mental-health/index.htm

Davenport, T., & Kalakota, R. (2019). The potential for artificial intelligence in healthcare. Future Healthcare Journal, 6(2), 94-98. https://doi.org/10.7861%2Ffuturehosp.6-2-94

Ebert, D. D. (2017). Prevention of mental health disorders using internet-and mobile-based interventions: A narrative review and recommendations for future research. Frontiers. https://www.frontiersin.org/articles/10.3389/fpsyt.2017.00116/full

Evans, G. W., Wells, N. M., & Moch, A. (2003). Housing and mental health: A review of the evidence and a methodological and conceptual critique. Journal of Social Issues, 59(3), 475-500. https://doi.org/10.1111/1540-4560.00074

Koneru, V. K., Weisman de Mamani, A. G., Flynn, P. M., & Betancourt, H. (2007). Acculturation and mental health: Current findings and recommendations for future research. Applied and Preventive Psychology, 12(2), 76-96. https://doi.org/10.1016/j.appsy.2007.07.016

Mackenzie, C. S., Gekoski, W. L., & Knox, V. J. (2006). Age, gender, and the underutilization of mental health services: The influence of help-seeking attitudes. Aging & Mental Health, 10(6), 574-582. https://doi.org/10.1080/13607860600641200

McCaffrey, J. D. (2018). Why a neural network is always better than logistic regression. Retrieved April 7, 2023, from https://jamesmccaffrey.wordpress.com/2018/07/07/why-a-neural-network-is-always-better-than-logistic-regression/

Miotto, R., Wang, F., Wang, S., Jiang, X., & Dudley, J. T. (2017). Deep learning for healthcare: Review, opportunities and challenges. Briefings in Bioinformatics, 19(6), 1236-1246. https://doi.org/10.1093/bib/bbx044

Rowan, K., Mcalpine, D. D., & Blewett, L. A. (2013). Access and cost barriers to mental health care, by insurance status, 1999–2010. Health Affairs, 32(10), 1723-1730. https://doi.org/10.1377%2Fhlthaff.2013.0133

Sava, F. A., Yates, B. T., Lupu, V., Szentagotai, A., & David, D. (2009). Cost-effectiveness and cost-utility of cognitive therapy, rational emotive behavioral therapy, and fluoxetine (prozac) in treating depression: A randomized clinical trial. Journal of Clinical Psychology, 65(1), 36-52. https://doi.org/10.1002/jclp.20550

Schaeffer, K. (2022, April 25). In CDC survey, 37% of U.S. high school students report regular mental health struggles during COVID-19. Pew Research Center. Retrieved September 26, 2022, from https://www.pewresearch.org/fact-tank/2022/04/25/in-cdc-survey-37-of-u-s-high-school-students-report-regular mental-health-struggles-during-covid-19/

Vernooij-Dassen, M. J. F. J., Moniz-Cook, E. D., Woods, R. T., Lepeleire, J. De, Leuschner, A., Zanetti, O., Rotrou, J. De, Kenny, G., Franco, M., Peters, V., & Iliffe, S. (2005). Factors affecting timely recognition and diagnosis of dementia across europe: From awareness to stigma. International Journal of Geriatric Psychiatry, 20(4), 377-386. https://doi.org/10.1002/gps.1302

Wu, D. T. Y., Xu, C., Kim, A., Bindhu, S., Mah, K. E., & Eckman, M. H. (2021). A scoping review of health information technology in clinician burnout. Applied Clinical Informatics, 12(03), 597-620. https://doi.org/10.1055/s-0041-1731399