Artificial intelligence enabled conversational agent for mental healthcare

Batyrkhan Omarov; Sergazi Narynov; Zhandos Zhumanov; Elmira Alzhanova; Aidana Gumar; Mariyam Khassanova

doi:10.53730/ijhs.v6n3.13239

Authors

Batyrkhan Omarov
batyahan@gmail.com
International University of Tourism and Hospitality, Turkistan, Kazakhstan
Sergazi Narynov Alem Research, Almaty, Kazakhstan
Zhandos Zhumanov Alem Research, Almaty, Kazakhstan
Elmira Alzhanova International University of Tourism and Hospitality, Turkistan, Kazakhstan
Aidana Gumar Alem Research, Almaty, Kazakhstan
Mariyam Khassanova Alem Research, Almaty, Kazakhstan

Keywords:

artificial intelligence, chatbot, conversational agents, healthcare, Mental health

Abstract

Conversational agents are software programs that can converse with users in the manner of a real-world conversation. Artificial intelligence (AI) is not complete without conversation modelling. The most difficult artificial intelligence endeavour since its start has been developing an effective chatbot application. Despite chatbots may do a variety of tasks, their main duty is to accurately understand human speech and respond appropriately. Previously, manual patterns and instructions or simple statistical methods were used to create chatbot architectures. Due to its improved capacity for training, end-to-end AI has replaced these models since 2015. The most popular technique for conversation simulation at the moment is the encoder-decoder recurrent neural network (RNN). The realm of language comprehension served as inspiration for this design. Until recently, some additions and changes dramatically enhanced chatbot conversational abilities. In this paper, we outline our research results into creating an interactive digital chatbot that may provide patients with psychological assistance. To build and train the chatbot, we used resources such Rasa Natural Language Processing (NLU) technology, which employs natural language processing (NLP) methods. The results of the investigation showed that selecting proper responses while conversing with patients had a more than 70% predictive performance.

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