Article Sidebar

Download
PDF
Statistic
Read Counter : 557 Download : 328

Main Article Content

Abstract

Binge drinking is one type of harmful alcohol use that has a variety of negative health impacts in both the drinker and others, either globally or in Malaysia. According to previous research, one in two current drinkers in Malaysia who are 13 years and older reported having engaged in binge drinking. Therefore, increased attention should be given to understand the drinking pattern of an individual and propose a solution that can help with addiction relapse. Thus, this study identified interventions that could assist alcohol relapse recovery and proposed a new generation of relapse prevention solution based on artificial intelligence (AI). By using a deep learning approach and machine learning based recommendation technique, it predicts the relapse rate of users, providing recovery consultation based on the user’s data and clinical data through a chatbot. This study involved helpful data collection, advanced data modeling, prediction analysis to support the alcohol relapse recovery journey. Hence, the proposed AI solution acted as a personalized virtual therapist to help the addicts stay sober. The objective is to present the design and realization of the AI based solution for sober journey. The proposed solution was tested with pilot study and significant benefits of virtual therapists for alcohol addiction relapse is reported in this paper.

Keywords

Artificial intelligence Alcohol addiction Relapse Prediction models Virtual therapist

Article Details

License

Authors who publish with this journal agree to the following terms:

  1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
  2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
  3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
How to Cite
Win, M. N., Han, L. W. ., Samson Chandresh Kumar, E. K. K. ., Keat, T. Y. ., & Ravana, S. D. (2022). AI-Based Personalized Virtual Therapist for Alcohol Relapse. Enthusiastic : International Journal of Applied Statistics and Data Science, 2(2), 82–96. https://doi.org/10.20885/enthusiastic.vol2.iss2.art3
Download Citation
Endnote/Zotero/Mendeley (RIS)
BibTeX

References

  1. Abdalrazak, H., et al. (2021). Review on Substances Abuse in Malaysia-Way Forward and Challenging. Annals of the Romanian Society for Cell Biology, 4930-4943.
  2. Ng, E. (2020). Alcohol Consumption in Malaysia. Retrieved 18 June 2022, from https://andatech.com.my/blogs/news/drug-and-alcohol-consumption-in-malaysia.
  3. Chhoa, K.H., Zakaria, H., and Abd Rahman, F.N. (2019). Problematic Alcohol Use and Depression in Secondary School Students Iin Miri, Malaysia. Pediatrics International, 61(3), 284-292.
  4. Hong, S.A., and Peltzer, K. (2019). Early Adolescent Patterns of Alcohol and Tobacco Use in Eight Association of South-East Asian Nations (ASEAN) Member States. Substance Use & Misuse, 54(2), 288-296.
  5. Murtarelli, G., Gregory, A., & Romenti, S. (2021). A Conversation-Based Perspective for Shaping Ethical Human–Machine Interactions: The Particular Challenge of Chatbots. Journal of Business Research, 129, 927-935.
  6. Laranjo, L., et al. (2018). Conversational Agents in Healthcare: A Systematic Review. Journal of the American Medical Informatics Association: JAMIA, 25(9), 1248–1258. https://doi.org/10.1093/jamia/ocy072.
  7. Battineni, G., Chintalapudi, N., and Amenta, F. (2020). AI Chatbot Design during an Epidemic Like the Novel Coronavirus. Healthcare (Basel, Switzerland), 8(2), 154. https://doi.org/10.3390/healthcare8020154.
  8. Brandtzaeg, P., and Folstad, A. (2018). Chatbots: Changing User Needs and Motivations. Interactions, 25(5), 38-43. https://doi.org/10.1145/3236669.
  9. Tait, R.J., Paz Castro, R., Kirkman, J., Moore, J. C., and Schaub, M. P. (2019). A Digital Intervention Addressing Alcohol Use Problems (the “Daybreak” Program): Quasi-Experimental Randomized Controlled Trial. Journal of Medical Internet Research, 21(9), e14967. https://doi.org/10.2196/14967.
  10. Mitchell, M.M., Mendelson, J., Gryczynski, J., Carswell, S.B., and Schwartz, R. P. (2020). A Novel Telehealth Platform for Alcohol Use Disorder Treatment: Preliminary Evidence of Reductions in Drinking. The American Journal of Drug and Alcohol Abuse, 46(3), 297–303. https://doi.org/10.1080/00952990.2019.1658197.
  11. Scott, C.K., Dennis, M.L., and Gustafson, D.H. (2017). Using Smartphones to Decrease Substance Use via Self-Monitoring and Recovery Support: Study Protocol for a Randomized Control Trial. Trials, 18(1), 374. https://doi.org/10.1186/s13063-017-2096-z.
  12. Adamopoulou, E., and Moussiades, L. (2020). Chatbots: History, Technology, and Applications. Machine Learning with Applications, 2. https://doi.org/10.1016/j.mlwa.2020.100006.
  13. Pandey, S., Sharma, S., and Wazir, S. (2022). Mental Healthcare Chatbot Based on Natural Language Processing and Deep Learning Approaches: Ted the Therapist. International Journal of Information Technology, 1-10.
  14. Abdellatif, A., Badran, K., Costa, D., and Shihab, E. (2021). A Comparison of Natural Language Understanding Platforms for Chatbots in Software Engineering. IEEE Transactions on Software Engineering, 1-1. doi: 10.1109/TSE.2021.3078384.
  15. Chu, E.T.-H., and Huang, Z.-Z. (2020). DBOS: A Dialog-Based Object Query System for Hospital Nurses. Sensors, 20(22), 6639. doi:10.3390/s20226639.
  16. Lair, N., Delgrange, C., Mugisha, D., Dussoux, J.-M., Oudeyer, P.-Y., and Dominey, P. F. (2020). User-in-the-Loop Adaptive Intent Detection for Instructable Digital Assistant. Proceedings of the 25th International Conference on Intelligent User Interfaces, Cagliari, Italy, March 2020. IUI. https://doi.org/10.1145/3377325.3377490.
  17. Abbet, C., et al. (2018). Churn Intent Detection in Multilingual Chatbot Conversations and Social Media. Proceedings of the 22nd Conference on Computational Natural Language Learning, Brussels, Belgium, October 2018. Association for Computational Linguistics.
  18. Tamersoy, A., Chau, D.H., and De Choudhury, M. (2017, July). Analysis of Smoking and Drinking Relapse in an Online Community. In Proceedings of the 2017 International Conference on Digital Health (pp. 33-42).
  19. Zhang, Y., Ramesh, A., Golbeck, J., Sridhar, D., and Getoor, L. (2018, April). A Structured Approach to Understanding Recovery and Relapse in AA. In Proceedings of the 2018 World Wide Web Conference (pp. 1205-1214).
  20. LuxuryRehab (n.d.) Find a luxury Rehab Centre in Malaysia. LuxuryRehab. https://luxuryrehabs.com/malaysia/.
  21. Brenier, J. (2018). Amplifying User Intelligence with Chatbot Feedback Loops. Chatbots Magazine. https://chatbotsmagazine.com/amplifying-user-intelligence-with-chatbot-feedback-loops-b8e6ded391ec.
  22. Louis, H. (2018). Chatbots Orchestration and Multilingual Challenges. IBM Cloud. https://www.ibm.com/blogs/cloud-archive/?p=163089&preview=true&preview_id=163089.
  23. Blanchard, B.E., Stevens, A.K., Sher, K.J., and Littlefield, A.K. (2020). Reexamining the Psychometric properties of the substance use risk profile scale. Assessment, 27(3), 454-471.
  24. Solares, J.R. et al. (2020). Deep Learning for Electronic Health Records: A Comparative Review of Multiple Deep Neural Architectures. Journal of Biomedical Informatics, 101, 103337.
  25. Najafabadi, M.K., Mahrin, M.N.R., Chuprat, S., and Sarkan, H. M. (2017). Improving the Accuracy of Collaborative Filtering Recommendations Using Clustering and Association Rules Mining on Implicit Data. Computers in Human Behavior, 67, 113-128.
  26. Reimers, N. and Gurevych, I. (2019). Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, Hong Kong, China, 3982–3992. doi:10.18653/v1/D19-1410.