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Mental healthcare chatbot based on natural language processing and deep learning approaches: Ted the therapist

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Abstract

Mental disorder is deliberated to be the top cause of Years Lived with Disability (YLD) with over 29% of the population affected. However, there is a shortage of mental healthcare providers and professionals to manage the huge population. Due to the extremely low number of mental healthcare providers available, one-on-one interaction with all the patients is not possible, which affects their treatment process. This effect severely hinders the treatment process which might result in suicidal behaviour and lead to the death of the patients in some cases. Therefore, there is a need for AI (Artificial Intelligence) techniques that help us to solve this issue. In this paper, we propose an AI web-based chatbot called “Ted” to assist people with mental health-related queries with the help of natural language processing and deep learning approaches. The user message is lemmatized and pre-processed in this step before being passed to the deep-learning model. Then, to specify the question category, an Artificial Neural Network with Softmax is used. This chatbot will allow the users to interact, use natural language to take input, and generate the appropriate response according to the input. The accuracy of our proposed chatbot is 98.13% in providing the appropriate response. In addition to this, “Ted” will help the patients who are reluctant to speak and get stigmatized by the presence of mental healthcare providers.

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References

  1. Newell A, Simon H (1956) The logic theory machine–a complex information processing system. IEEE Trans Inf Theory 2(3):61–79. https://doi.org/10.1109/tit.1956.1056797

    Article  Google Scholar 

  2. Weizenbaum J (2021) “ELIZA: a computer program for the study of natural language communication between man and machine. Commun ACM. https://doi.org/10.1145/365153.365168

    Article  MATH  Google Scholar 

  3. World Health Organization: WHO (2018) Mental health: strengthening our response. Who.int. https://www.who.int/news-room/fact-sheets/detail/mental-health-strengthening-our-response. Accessed 07 Nov 2021

  4. “What Is a Chatbot?,” Investopedia (2021) https://www.investopedia.com/terms/c/chatbot.asp. Accessed 07 Nov 2021

  5. Butryn T, Bryant L, Marchionni C, Sholevar F (2017) The shortage of psychiatrists and other mental health providers: causes, current state, and potential solutions. Int J Acad Med 3(1):5. https://doi.org/10.4103/IJAM.IJAM_49_17

    Article  Google Scholar 

  6. Watson A (2010) How to succeed with NLP: Go from good to great at work. John Wiley & Sons, New Jeresy

    Google Scholar 

  7. Bardam JE et al (2013) Designing mobile health technology for bipolar disorder. Proc SIGCHI Conf Hum Factors Comput Syst. https://doi.org/10.1145/2470654.2481364

    Article  Google Scholar 

  8. Tielman M, van Meggelen M, Neerincx MA, Brinkman W-P (2015) An ontology-based question system for a virtual coach assisting in trauma recollection. Intell Virtual Agents. https://doi.org/10.1007/978-3-319-21996-7_2

    Article  Google Scholar 

  9. Crutzen R, Peters G-JY, Portugal SD, Fisser EM, Grolleman JJ (2011) An artificially intelligent chat agent that answers adolescents’ questions related to sex, drugs, and alcohol: an exploratory study. J Adolesc Health 48(5):514–519. https://doi.org/10.1016/j.jadohealth.2010.09.002

    Article  Google Scholar 

  10. Yaghoubzadeh R, Kramer M, Pitsch K, Kopp S (2013) Virtual agents as daily assistants for elderly or cognitively impaired people. Intell Virtual Agents. https://doi.org/10.1007/978-3-642-40415-3_7

    Article  Google Scholar 

  11. Zahra RS, Schubert LK, Kane B, Ali MR, Orden V, Ma T (2021) Dialogue design and management for multi-session casual conversation with older adults. arXiv.org. https://arxiv.org/abs/1901.06620. Accessed 14 Nov 2021

  12. Solon O (2016) Karim the AI delivers psychological support to Syrian refugees.  The Guardian vol 22.

  13. Jayanthi K, Mohan S (2022) An integrated framework for emotion recognition using speech and static images with deep classifier fusion approach. Int J Inf Technol. https://doi.org/10.1007/s41870-022-00900-5

    Article  Google Scholar 

  14. Rashida M, Habib MA (2021) A smartphone-based wander management system for Bangla speaking patients with Alzheimer’s disease. Int J Inf Technol 13:2543–2550. https://doi.org/10.1007/s41870-021-00761-4

    Article  Google Scholar 

  15. Koch J, Lucero A, Hegemann L,  Oulasvirta A (2019) May AI? Design ideation with cooperative contextual bandits. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (pp. 1-12).

  16. Relational Agent for Mental Health (2021) Woebot Health. https://woebothealth.com/. Accessed 14 Nov 2021

  17. Wysa-Everyday Mental Health (2020). https://www.wysa.io/. Accessed 14 Nov 2021

  18. AbleTo (2021) Pioneering high-quality virtual behavioral health care. AbleTo. https://www.ableto.com/. Accessed 14 Nov 2021

  19. Kaggle (2022) Kaggle: your machine learning and data science community. Kaggle. https://www.kaggle.com/. Accessed 20 Feb 2022

  20. Github (2021) GitHub: where the world builds software. GitHub. Internet. https://github.com/. Accessed 20 Feb 2022

  21. Reddit (2022) Reddit-dive into anything. https://www.reddit.com/. Accessed 20 Feb 2022

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Correspondence to Sumit Pandey.

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Pandey, S., Sharma, S. & Wazir, S. Mental healthcare chatbot based on natural language processing and deep learning approaches: Ted the therapist. Int. j. inf. tecnol. 14, 3757–3766 (2022). https://doi.org/10.1007/s41870-022-00999-6

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  • DOI: https://doi.org/10.1007/s41870-022-00999-6

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