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CautionSuicide: A Deep Learning Based Approach for Detecting Suicidal Ideation in Real Time Chatbot Conversation (2401.01023v1)

Published 2 Jan 2024 in cs.HC and cs.LG

Abstract: Suicide is recognized as one of the most serious concerns in the modern society. Suicide causes tragedy that affects countries, communities, and families. There are many factors that lead to suicidal ideations. Early detection of suicidal ideations can help to prevent suicide occurrence by providing the victim with the required professional support, especially when the victim does not recognize the danger of having suicidal ideations. As technology usage has increased, people share and express their ideations digitally via social media, chatbots, and other digital platforms. In this paper, we proposed a novel, simple deep learning-based model to detect suicidal ideations in digital content, mainly focusing on chatbots as the primary data source. In addition, we provide a framework that employs the proposed suicide detection integration with a chatbot-based support system.

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References (39)
  1. G. Turecki, D. A. Brent, D. Gunnell, R. C. O’Connor, M. A. Oquendo, J. Pirkis, and B. H. Stanley, “Suicide and suicide risk,” Nature reviews Disease primers, vol. 5, no. 1, p. 74, 2019.
  2. C. for Disease Control, Prevention, and Others, “Suicide data and statistics.” https://www.cdc.gov/suicide/suicide-data-statistics.html. Accessed: 2023-12-28.
  3. E. Chesney, G. M. Goodwin, and S. Fazel, “Risks of all-cause and suicide mortality in mental disorders: a meta-review,” World psychiatry, vol. 13, no. 2, pp. 153–160, 2014.
  4. F. Rivara, A. Adhia, V. Lyons, A. Massey, B. Mills, E. Morgan, M. Simckes, and A. Rowhani-Rahbar, “The effects of violence on health,” Health Affairs, vol. 38, no. 10, pp. 1622–1629, 2019.
  5. S. W. Azumah, N. Elsayed, Z. ElSayed, and M. Ozer, “Cyberbullying in text content detection: An analytical review,” arXiv preprint arXiv:2303.10502, 2023.
  6. S. Hinduja and J. W. Patchin, “Bullying, cyberbullying, and suicide,” Archives of suicide research, vol. 14, no. 3, pp. 206–221, 2010.
  7. S. Hinduja and J. W. Patchin, “Connecting adolescent suicide to the severity of bullying and cyberbullying,” Journal of school violence, vol. 18, no. 3, pp. 333–346, 2019.
  8. W. H. Organization, “Suicide.” https://www.who.int/news-room/fact-sheets/detail/suicide. Accessed: 2023-12-24.
  9. W. H. Organization, “Suicide.” https://www.nimh.nih.gov/health/statistics/suicide. Accessed: 2023-12-24.
  10. C. for Disease Control, Prevention, and Others, “Fatal injury trends.” https://www.cdc.gov/injury/wisqars/fatal/trends.html. Accessed: 2023-12-28.
  11. D. M. Stone, K. M. Holland, B. N. Bartholow, A. E. Crosby, S. P. Davis, and N. Wilkins, “Preventing suicide: A technical package of policies, programs, and practice,” 2017.
  12. World Health Organization, 2004.
  13. J. J. Mann, A. Apter, J. Bertolote, A. Beautrais, D. Currier, A. Haas, U. Hegerl, J. Lonnqvist, K. Malone, A. Marusic, et al., “Suicide prevention strategies: a systematic review,” Jama, vol. 294, no. 16, pp. 2064–2074, 2005.
  14. S. Al-Halabí and E. Fonseca-Pedrero, “Suicidal behavior prevention: The time to act is now,” Clínica y Salud, vol. 32, no. 2, pp. 89–92, 2021.
  15. A. Cole-King, G. Green, L. Gask, K. Hines, and S. Platt, “Suicide mitigation: a compassionate approach to suicide prevention,” Advances in psychiatric treatment, vol. 19, no. 4, pp. 276–283, 2013.
  16. J. E. DeVylder, E. Thompson, G. Reeves, and J. Schiffman, “Psychotic experiences as indicators of suicidal ideation in a non-clinical college sample,” Psychiatry research, vol. 226, no. 2-3, pp. 489–493, 2015.
  17. S. Ji, S. Pan, X. Li, E. Cambria, G. Long, and Z. Huang, “Suicidal ideation detection: A review of machine learning methods and applications,” IEEE Transactions on Computational Social Systems, vol. 8, no. 1, pp. 214–226, 2020.
  18. J. Wosik, M. Fudim, B. Cameron, Z. F. Gellad, A. Cho, D. Phinney, S. Curtis, M. Roman, E. G. Poon, J. Ferranti, et al., “Telehealth transformation: Covid-19 and the rise of virtual care,” Journal of the American Medical Informatics Association, vol. 27, no. 6, pp. 957–962, 2020.
  19. K. M. McGrail, M. A. Ahuja, and C. A. Leaver, “Virtual visits and patient-centered care: results of a patient survey and observational study,” Journal of medical Internet research, vol. 19, no. 5, p. e177, 2017.
  20. D. M. Mann, J. Chen, R. Chunara, P. A. Testa, and O. Nov, “Covid-19 transforms health care through telemedicine: evidence from the field,” Journal of the American Medical Informatics Association, vol. 27, no. 7, pp. 1132–1135, 2020.
  21. A. A. Rizzo, J. G. Buckwalter, and C. van der Zaag, “Virtual environment applications in clinical neuropsychology,” in Handbook of virtual environments, pp. 1067–1104, CRC Press, 2002.
  22. A. A. A. Weißensteiner, “Chatbots as an approach for a faster enquiry handling process in the service industry,” Signature, vol. 12, no. 04, 2018.
  23. B. Luo, R. Y. Lau, C. Li, and Y.-W. Si, “A critical review of state-of-the-art chatbot designs and applications,” Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, vol. 12, no. 1, p. e1434, 2022.
  24. J. Skrebeca, P. Kalniete, J. Goldbergs, L. Pitkevica, D. Tihomirova, and A. Romanovs, “Modern development trends of chatbots using artificial intelligence (ai),” in 2021 62nd International Scientific Conference on Information Technology and Management Science of Riga Technical University (ITMS), pp. 1–6, IEEE, 2021.
  25. N. Elsayed, Z. ElSayed, and M. Ozer, “Machine learning for early mental health support and offenders correction,” in The International FLAIRS Conference Proceedings, vol. 36, 2023.
  26. T. M. Fonseka, V. Bhat, and S. H. Kennedy, “The utility of artificial intelligence in suicide risk prediction and the management of suicidal behaviors,” Australian & New Zealand Journal of Psychiatry, vol. 53, no. 10, pp. 954–964, 2019.
  27. L. Martinengo, L. Van Galen, E. Lum, M. Kowalski, M. Subramaniam, and J. Car, “Suicide prevention and depression apps’ suicide risk assessment and management: a systematic assessment of adherence to clinical guidelines,” BMC medicine, vol. 17, no. 1, pp. 1–12, 2019.
  28. J. Chung, C. Gulcehre, K. Cho, and Y. Bengio, “Empirical evaluation of gated recurrent neural networks on sequence modeling,” arXiv preprint arXiv:1412.3555, 2014.
  29. N. Elsayed, A. S. Maida, and M. Bayoumi, “Deep gated recurrent and convolutional network hybrid model for univariate time series classification,” arXiv preprint arXiv:1812.07683, 2018.
  30. Y. Wang, W. Liao, and Y. Chang, “Gated recurrent unit network-based short-term photovoltaic forecasting,” Energies, vol. 11, no. 8, p. 2163, 2018.
  31. R. Haque, N. Islam, M. Islam, and M. M. Ahsan, “A comparative analysis on suicidal ideation detection using nlp, machine, and deep learning,” Technologies, vol. 10, no. 3, p. 57, 2022.
  32. L. Wu, S. Li, C.-J. Hsieh, and J. L. Sharpnack, “Stochastic shared embeddings: Data-driven regularization of embedding layers,” Advances in Neural Information Processing Systems, vol. 32, 2019.
  33. N. KOMATI, “Suicide and depression detection.” https://www.kaggle.com/datasets/nikhileswarkomati/suicide-watch. Accessed: 2023-12-24.
  34. S. Alam and N. Yao, “The impact of preprocessing steps on the accuracy of machine learning algorithms in sentiment analysis,” Computational and Mathematical Organization Theory, vol. 25, pp. 319–335, 2019.
  35. S. Vijayarani, M. J. Ilamathi, M. Nithya, et al., “Preprocessing techniques for text mining-an overview,” International Journal of Computer Science & Communication Networks, vol. 5, no. 1, pp. 7–16, 2015.
  36. MIT press, 2016.
  37. X. Glorot and Y. Bengio, “Understanding the difficulty of training deep feedforward neural networks,” in Proceedings of the thirteenth international conference on artificial intelligence and statistics, pp. 249–256, JMLR Workshop and Conference Proceedings, 2010.
  38. E. Vorontsov, C. Trabelsi, S. Kadoury, and C. Pal, “On orthogonality and learning recurrent networks with long term dependencies,” in International Conference on Machine Learning, pp. 3570–3578, PMLR, 2017.
  39. D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” arXiv preprint arXiv:1412.6980, 2014.

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