Depression Therapy Chat-Bot using Natural Language Processing

作者
Roshani Raut,Sapana Kolambe,Pradnya Krishnanath Borkar,Anita Shingade,Monali Gulhane
出处
期刊:International Journal of Intelligent Systems and Applications in Engineering [International Journal of Intelligent Systems and Applications in Engineering]
摘要

Millions of people worldwide suffer from the widespread and crippling mental health illness of depression. Whilst many people find standard therapy methods like cognitive-behavioral therapy (CBT) and medication useful, not everyone can access or afford them. A potential substitute for providing depression therapy is chatbots, conversational agents created to mimic human discourse. In a randomized controlled trial, the purpose of this study was to assess the effectiveness of a chatbot-based therapy for depression. The wait-list control group or the chatbot therapy group was chosen at random for the participants. The control group received no treatment during this time, while the chatbot therapy group received 8 weeks of therapy via a chatbot. The Patient Health Questionnaire-9 (PHQ-9) and the Beck Depression Inventory-II data revealed that the chatbot therapy group experienced significantly fewer depressive symptoms than the control group (BDI-II). High levels of satisfaction with the therapy and the chatbot interface were also expressed by participants in the chatbot therapy group. These results imply that chatbot-based treatment may be a promising substitute for providing depression therapy, especially for people who might not have access to conventional therapeutic techniques. We anticipate that the chatbot therapy group will significantly outperform the control group in terms of anxiety levels and quality of life. The model returned back with the accuracy of 75%. These findings collectively imply that chatbots have the potential to be a useful and practical tool for providing depression therapy

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