Early Depression Detection for Young Adults with a Psychiatric and AI Interdisciplinary Multimodal Framework
During COVID-19, the depression rate has increased dramatically. Young adults are most vulnerable to the mental health effects of the pandemic. Lower-income families have a higher ratio to be diagnosed with depression than the general population, but less access to clinics. This research aims to achieve early depression detection at low cost, large scale, and high accuracy with an interdisciplinary approach by incorporating clinical practices defined by American Psychiatric Association (APA) as well as multimodal AI framework. The proposed approach detected the nine depression symptoms with Natural Language Processing sentiment analysis and a symptom-based Lexicon uniquely designed for young adults. The experiments were conducted on the multimedia survey results from adolescents and young adults and unbiased Twitter communications. The result was further aggregated with the facial emotional cues analyzed by the Convolutional Neural Network on the multimedia survey videos. Five experiments each conducted on 10k data entries reached consistent results with an average accuracy of 88.31%, higher than the existing natural language analysis models. This approach can reach 300+ million daily active Twitter users and is highly accessible by low-income populations to promote early depression detection to raise awareness in adolescents and young adults and reveal complementary cues to assist clinical depression diagnosis.
[1] “Depression.” https://www.who.int/news-room/fact-sheets/detail/depression (accessed Apr. 29, 2021).
[2] S. Avenevoli, J. Swendsen, J.-P. He, M. Burstein, and K. Merikangas, “Major Depression in the National Comorbidity Survey- Adolescent Supplement: Prevalence, Correlates, and Treatment,” J. Am. Acad. Child Adolesc. Psychiatry, vol. 54, Oct. 2014, doi: 10.1016/j.jaac.2014.10.010.
[3] R. Feintzeig, “Is It OK to Reveal Your Anxiety or Depression to Your Boss?,” Wall Street Journal, Sep. 13, 2020.
[4] “Depression,” Cmu.edu, 2021. https://www-sciencedirect-com.proxy.library.cmu.edu/science/article/pii/S0140673618319482?via%3Dihub (accessed Apr. 29, 2021).
[5] P. Arora and P. Arora, “Mining Twitter Data for Depression Detection,” IEEE Xplore, 2019. https://ieeexplore.ieee.org/document/8938353/authors#authors (accessed Apr. 29, 2021).
[6] S. Moon, L. Neves, and V. Carvalho, “Multimodal Named Entity Recognition for Short Social Media Posts,” arXiv:1802.07862 (cs), Feb. 2018, Accessed: Apr. 29, 2021. (Online). Available: https://arxiv.org/abs/1802.07862.
[7] J. Howard and S. Ruder, “Universal Language Model Fine-tuning for Text Classification,” Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2018, doi: 10.18653/v1/p18-1031.
[8] A. Benton, M. Mitchell, and D. Hovy, “Multitask Learning for Mental Health Conditions with Limited Social Media Data,” ACLWeb, Apr. 01, 2017. https://www.aclweb.org/anthology/E17-1015/ (accessed Apr. 29, 2021).
[9] K. Simonyan and A. Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition,” arXiv.org, 2014. https://arxiv.org/abs/1409.1556.
[10] J. Chung, C. Gulcehre, K. Cho, and Y. Bengio, “Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling,” arXiv.org, 2014. https://arxiv.org/abs/1412.3555.
[11] CESD-R, “Center for Epidemiologic Studies Depression Scale Revised Online Depression Assessment” (Online) Available: https://cesd-r.com
[12] Center for Epidemiologic Studies - Depression Scale (CES-D) | Measurement Instrument Database for the Social Sciences. (Online) Available: https://www.midss.org/content/center-epidemiologic-studies-depression-scale-ces-d
[13] PHQ-9 Depression Test Questionnaire (Online) Available: https://patient.info/doctor/patient-health-questionnaire-phq-9 and https://www.hiv.uw.edu/page/mental-health-screening/phq-9
[14] Ariel Shensa, Jaime E. Sidani, Cesar G. Escobar-Viera, et al. “Emotional support from social media and face-to-face relationships: Associations with depression risk among young adults” (Online) Available: https://www.tandfonline.com/doi/full/10.1080/09638237.2019.1581357?scroll=top&needAccess=true
[15] Y. Ophir, C. S. C. Asterhan, and B. B. Schwarz, “Unfolding the notes from the walls: Adolescents’ depression manifestations on Facebook,” Computers in Human Behavior, vol. 72, pp. 96–107, Jul. 2017, doi: 10.1016/j.chb.2017.02.013.
[16] K. Harvey, “Disclosures of depression,” International Journal of Corpus Linguistics, vol. 17, no. 3, pp. 349–379, Dec. 2012, doi: 10.1075/ijcl.17.3.03har.
[17] R. Dryden-Edwards, “Teen Depression Facts, Treatment, Symptoms, Statistics & Tests,” MedicineNet, 2019. https://www.medicinenet.com/teen_depression/article.htm.
[18] Thomas, Lauren. What is a longitudinal study?. (Online) Available: https://www.scribbr.com/methodology/longitudinal-study
[19] B. L. Hankin, L. Y. Abramson, T. E. Moffitt, P. A. Silva, R. Mcgee, and K. E. Angell, “Development of depression from preadolescence to young adulthood: Emerging gender differences in a 10-year longitudinal study.,” Journal of Abnormal Psychology, vol. 107, no. 1, pp. 128–140, 1998.
[20] S. E. Gilman, E. Sucha, M. Kingsbury, N. J. Horton, J. M. Murphy, and I. Colman, “Depression and mortality in a longitudinal study: 1952–2011,” CMAJ, 23-Oct-2017. (Online). Available: https://www.cmaj.ca/content/189/42/E1304.short. (Accessed: 17-May-2021).
[21] “APA PsycNet,” psycnet.apa.org. https://psycnet.apa.org/record/2013-14907-000.
[22] “NIMH» Depression Basics,” www.nimh.nih.gov. https://www.nimh.nih.gov/health/publications/depression/.
[1] “Depression.” https://www.who.int/news-room/fact-sheets/detail/depression (accessed Apr. 29, 2021).
[2] S. Avenevoli, J. Swendsen, J.-P. He, M. Burstein, and K. Merikangas, “Major Depression in the National Comorbidity Survey- Adolescent Supplement: Prevalence, Correlates, and Treatment,” J. Am. Acad. Child Adolesc. Psychiatry, vol. 54, Oct. 2014, doi: 10.1016/j.jaac.2014.10.010.
[3] R. Feintzeig, “Is It OK to Reveal Your Anxiety or Depression to Your Boss?,” Wall Street Journal, Sep. 13, 2020.
[4] “Depression,” Cmu.edu, 2021. https://www-sciencedirect-com.proxy.library.cmu.edu/science/article/pii/S0140673618319482?via%3Dihub (accessed Apr. 29, 2021).
[5] P. Arora and P. Arora, “Mining Twitter Data for Depression Detection,” IEEE Xplore, 2019. https://ieeexplore.ieee.org/document/8938353/authors#authors (accessed Apr. 29, 2021).
[6] S. Moon, L. Neves, and V. Carvalho, “Multimodal Named Entity Recognition for Short Social Media Posts,” arXiv:1802.07862 (cs), Feb. 2018, Accessed: Apr. 29, 2021. (Online). Available: https://arxiv.org/abs/1802.07862.
[7] J. Howard and S. Ruder, “Universal Language Model Fine-tuning for Text Classification,” Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2018, doi: 10.18653/v1/p18-1031.
[8] A. Benton, M. Mitchell, and D. Hovy, “Multitask Learning for Mental Health Conditions with Limited Social Media Data,” ACLWeb, Apr. 01, 2017. https://www.aclweb.org/anthology/E17-1015/ (accessed Apr. 29, 2021).
[9] K. Simonyan and A. Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition,” arXiv.org, 2014. https://arxiv.org/abs/1409.1556.
[10] J. Chung, C. Gulcehre, K. Cho, and Y. Bengio, “Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling,” arXiv.org, 2014. https://arxiv.org/abs/1412.3555.
[11] CESD-R, “Center for Epidemiologic Studies Depression Scale Revised Online Depression Assessment” (Online) Available: https://cesd-r.com
[12] Center for Epidemiologic Studies - Depression Scale (CES-D) | Measurement Instrument Database for the Social Sciences. (Online) Available: https://www.midss.org/content/center-epidemiologic-studies-depression-scale-ces-d
[13] PHQ-9 Depression Test Questionnaire (Online) Available: https://patient.info/doctor/patient-health-questionnaire-phq-9 and https://www.hiv.uw.edu/page/mental-health-screening/phq-9
[14] Ariel Shensa, Jaime E. Sidani, Cesar G. Escobar-Viera, et al. “Emotional support from social media and face-to-face relationships: Associations with depression risk among young adults” (Online) Available: https://www.tandfonline.com/doi/full/10.1080/09638237.2019.1581357?scroll=top&needAccess=true
[15] Y. Ophir, C. S. C. Asterhan, and B. B. Schwarz, “Unfolding the notes from the walls: Adolescents’ depression manifestations on Facebook,” Computers in Human Behavior, vol. 72, pp. 96–107, Jul. 2017, doi: 10.1016/j.chb.2017.02.013.
[16] K. Harvey, “Disclosures of depression,” International Journal of Corpus Linguistics, vol. 17, no. 3, pp. 349–379, Dec. 2012, doi: 10.1075/ijcl.17.3.03har.
[17] R. Dryden-Edwards, “Teen Depression Facts, Treatment, Symptoms, Statistics & Tests,” MedicineNet, 2019. https://www.medicinenet.com/teen_depression/article.htm.
[18] Thomas, Lauren. What is a longitudinal study?. (Online) Available: https://www.scribbr.com/methodology/longitudinal-study
[19] B. L. Hankin, L. Y. Abramson, T. E. Moffitt, P. A. Silva, R. Mcgee, and K. E. Angell, “Development of depression from preadolescence to young adulthood: Emerging gender differences in a 10-year longitudinal study.,” Journal of Abnormal Psychology, vol. 107, no. 1, pp. 128–140, 1998.
[20] S. E. Gilman, E. Sucha, M. Kingsbury, N. J. Horton, J. M. Murphy, and I. Colman, “Depression and mortality in a longitudinal study: 1952–2011,” CMAJ, 23-Oct-2017. (Online). Available: https://www.cmaj.ca/content/189/42/E1304.short. (Accessed: 17-May-2021).
[21] “APA PsycNet,” psycnet.apa.org. https://psycnet.apa.org/record/2013-14907-000.
[22] “NIMH» Depression Basics,” www.nimh.nih.gov. https://www.nimh.nih.gov/health/publications/depression/.
@article{"International Journal of Medical, Medicine and Health Sciences:80385", author = "Raymond Xu and Ashley Hua and Andrew Wang and Yuru Lin", title = "Early Depression Detection for Young Adults with a Psychiatric and AI Interdisciplinary Multimodal Framework", abstract = "During COVID-19, the depression rate has increased dramatically. Young adults are most vulnerable to the mental health effects of the pandemic. Lower-income families have a higher ratio to be diagnosed with depression than the general population, but less access to clinics. This research aims to achieve early depression detection at low cost, large scale, and high accuracy with an interdisciplinary approach by incorporating clinical practices defined by American Psychiatric Association (APA) as well as multimodal AI framework. The proposed approach detected the nine depression symptoms with Natural Language Processing sentiment analysis and a symptom-based Lexicon uniquely designed for young adults. The experiments were conducted on the multimedia survey results from adolescents and young adults and unbiased Twitter communications. The result was further aggregated with the facial emotional cues analyzed by the Convolutional Neural Network on the multimedia survey videos. Five experiments each conducted on 10k data entries reached consistent results with an average accuracy of 88.31%, higher than the existing natural language analysis models. This approach can reach 300+ million daily active Twitter users and is highly accessible by low-income populations to promote early depression detection to raise awareness in adolescents and young adults and reveal complementary cues to assist clinical depression diagnosis.
", keywords = "Artificial intelligence, depression detection, facial emotion recognition, natural language processing, mental disorder.", volume = "15", number = "6", pages = "154-7", }
