Abstract
The application of Artificial Intelligence (AI) in the assessment and treatment of mental health has gained momentum in recent years due to the evidential development of chatbots. There are promising outcomes from recent attempts such as facilitation to detect the depression level in patients’ profiles, which have improved the aspiration of finding a solution to assist medical professionals in detecting depression. However, experts believe the promise is still far from the expectations since most of the chatbots found in literature has conscious decision from selectable answer. In addition, the participants are required to have longer period of the interactions with the chatbot which suffer great losses of the participation. Furthermore, the user privacy and scientific evaluations of early depression detection are not guaranteed due to the customized chatbot platforms. Motivated by these, we proposed and developed DEPRA based on contemporary bot platforms with early depression detection to tackle the mental health symptoms. DEPRA is built on Dialogflow as a conversation interface and uses personalized utterances collected from a focused group to train it. Moreover, the interaction time was reduced remarkably by the setup of DEPRA. A structured early detection depression interview guide for the Hamilton Depression Scale (SIGH-D) and Inventory of Depressive Symptomatology (IDS-C) underpins the formation. DEPRA can act as the proxy between the health professional and the patient. Moreover, the DEPRA integrated with social network platform which provide convenience of the attractions for the participants. More than 40 participants interact with DEPRA and the analysis of their response establishes the promise of its use in mass screening for early detection of depression. User experience survey demonstrates that the overall user satisfaction level is approbatory.
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References
Akbari, H., Sadiq, M.T., Payan, M., Esmaili, S.S., Baghri, H., Bagheri, H.: Depression detection based on geometrical features extracted from SODP shape of EEG signals and binary PSO. Traitement du Signal 38(1), 13–26 (2021). https://doi.org/10.18280/ts.380102
Australia, Y.B.: Australian bureau of statistics. Canberra, Australia 161 (2008)
Bickmore, T.W., Mitchell, S.E., Jack, B.W., Paasche-Orlow, M.K., Pfeifer, L.M., ODonnell, J.: Response to a relational agent by hospital patients with depressive symptoms. Interact. Comput. 22(4), 289–298 (2010). https://doi.org/10.1016/j.intcom.2009.12.001
Bickmore, T.W., Puskar, K., Schlenk, E.A., Pfeifer, L.M., Sereika, S.M.: Maintaining reality: Relational agents for antipsychotic medication adherence. Interact. Comput. 22(4), 276–288 (2010). https://doi.org/10.1016/j.intcom.2010.02.001
Cacheda, F., Fernandez, D., Novoa, F.J., Carneiro, V., et al.: Early detection of depression: social network analysis and random forest techniques. J. Med. Internet Res. 21(6), e12554 (2019). https://doi.org/10.2196/12554
Fitzpatrick, K.K., Darcy, A., Vierhile, M.: 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 Ment. Health 4(2), e19 (2017)
Gardiner, P.M., et al.: Engaging women with an embodied conversational agent to deliver mindfulness and lifestyle recommendations: a feasibility randomized control trial. Patient Educ. Counseling 100(9), 1720–1729 (2017). https://doi.org/10.1016/j.pec.2017.04.015
INITIATIVE, S., TARGET, W.G.: Universal health coverage for mental health
Lucas, G.M., et al.: Reporting mental health symptoms: Breaking down barriers to care with virtual human interviewers. Front. Robot. AI 4, 51 (2017)
Philip, P., et al.: Virtual human as a new diagnostic tool, a proof of concept study in the field of major depressive disorders. Sci. Rep. 7(1), 1–7 (2017)
Podrazhansky, A., Zhang, H., Han, M., He, S.: A chatbot-based mobile application to predict and early-prevent human mental illness. In: Proceedings of the 2020 ACM Southeast Conference, pp. 311–312. ACM SE 2020, Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3374135.3385319
Sharma, B., Puri, H., Rawat, D.: Digital psychiatry-curbing depression using therapy chatbot and depression analysis. In: 2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT), pp. 627–631. IEEE (2018)
Sharma, T., Parihar, J., Singh, S.: Intelligent chatbot for prediction and management of stress. In: 2021 11th International Conference on Cloud Computing, Data Science Engineering (Confluence), pp. 937–941 (2021). https://doi.org/10.1109/Confluence51648.2021.9377091
Shinozaki, T., Yamamoto, Y., Tsuruta, S.: Context-based counselor agent for software development ecosystem. Computing 97(1), 3–28 (2015)
Stankevich, M., Latyshev, A., Kuminskaya, E., Smirnov, I., Grigoriev, O.: Depression detection from social media texts. In: Data Analytics and Management in Data Intensive Domains: I International Conference DADID/RCDL 2019, pp. 352–496 (2019)
Tielman, M.L., Neerincx, M.A., Bidarra, R., Kybartas, B., Brinkman, W.P.: A therapy system for post-traumatic stress disorder using a virtual agent and virtual storytelling to reconstruct traumatic memories. J. Med. Syst. 41(8), 1–10 (2017). https://doi.org/10.1007/s10916-017-0771-y
Tielman, M.L., Neerincx, M.A., van Meggelen, M., Franken, I., Brinkman, W.P.: How should a virtual agent present psychoeducation? Influence of verbal and textual presentation on adherence. Technol. Health Care Official J. Eur. Soc. Eng. Med. 25(6), 1081–1096 (2017)
Vaidyam, A.N., Wisniewski, H., Halamka, J.D., Kashavan, M.S., Torous, J.B.: Chatbots and conversational agents in mental health: a review of the psychiatric landscape. Canadian J. Psych. Revue. canadienne de psychiatrie 64(7), 456–464 (2019). https://doi.org/10.1177/0706743719828977
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Kaywan, P., Ahmed, K., Miao, Y., Ibaida, A., Gu, B. (2021). DEPRA: An Early Depression Detection Analysis Chatbot. In: Siuly, S., Wang, H., Chen, L., Guo, Y., Xing, C. (eds) Health Information Science. HIS 2021. Lecture Notes in Computer Science(), vol 13079. Springer, Cham. https://doi.org/10.1007/978-3-030-90885-0_18
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