Intelligent Counselling Bot Using Ranking Algorithm in AI

Sakthivel, V.; Srihari, K.; Karthik, S.; D, Anisha C.

doi:10.1109/icsns.2018.8573642

Cited by 3 publications

(1 citation statement)

References 7 publications

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“…Many text understanding approaches can fall under pattern matching. The different pattern matching methods reported in the included studies are as follows: keyword matching or string matching, where specific keywords or strings of the user input are matched to scripts stored in the database, which was used in 11 studies [ 10 - 15 , 32 , 33 , 46 , 48 , 50 ], or Extensible Markup Language (XML) variants, such as Artificial Intelligence Markup Language (AIML), which were used in 7 studies [ 16 , 17 , 34 - 37 , 47 ]. The second most popular approach was the use of machine learning methods for text understanding, which was used in 6 studies.…”

Section: Resultsmentioning

confidence: 99%

Technical Aspects of Developing Chatbots for Medical Applications: Scoping Review

et al. 2020

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Background Chatbots are applications that can conduct natural language conversations with users. In the medical field, chatbots have been developed and used to serve different purposes. They provide patients with timely information that can be critical in some scenarios, such as access to mental health resources. Since the development of the first chatbot, ELIZA, in the late 1960s, much effort has followed to produce chatbots for various health purposes developed in different ways. Objective This study aimed to explore the technical aspects and development methodologies associated with chatbots used in the medical field to explain the best methods of development and support chatbot development researchers on their future work. Methods We searched for relevant articles in 8 literature databases (IEEE, ACM, Springer, ScienceDirect, Embase, MEDLINE, PsycINFO, and Google Scholar). We also performed forward and backward reference checking of the selected articles. Study selection was performed by one reviewer, and 50% of the selected studies were randomly checked by a second reviewer. A narrative approach was used for result synthesis. Chatbots were classified based on the different technical aspects of their development. The main chatbot components were identified in addition to the different techniques for implementing each module. Results The original search returned 2481 publications, of which we identified 45 studies that matched our inclusion and exclusion criteria. The most common language of communication between users and chatbots was English (n=23). We identified 4 main modules: text understanding module, dialog management module, database layer, and text generation module. The most common technique for developing text understanding and dialogue management is the pattern matching method (n=18 and n=25, respectively). The most common text generation is fixed output (n=36). Very few studies relied on generating original output. Most studies kept a medical knowledge base to be used by the chatbot for different purposes throughout the conversations. A few studies kept conversation scripts and collected user data and previous conversations. Conclusions Many chatbots have been developed for medical use, at an increasing rate. There is a recent, apparent shift in adopting machine learning–based approaches for developing chatbot systems. Further research can be conducted to link clinical outcomes to different chatbot development techniques and technical characteristics.

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Section: Resultsmentioning

confidence: 99%

Technical Aspects of Developing Chatbots for Medical Applications: Scoping Review

et al. 2020

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Chat-Bot as a Way to Support Victims of School Bullying

Rogovets,

Mazurkevitch,

Suvorova

et al. 2023

Современная Зарубежная Психология

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<p style="text-align: justify;">This article is devoted to an overview of the sources on therapeutic chatbots, as well as a description of the problem of creating an anti-bullying chatbot. Most chatbots work on the basis of cognitive-behavioral psychotherapy and are aimed at preventing depressive vessels in adults. There are no anti-bullying chatbots yet. There are at least two reasons: the exceptional situation of bullying and the legal aspects of working with teenagers. The specificity of the bullying situation is working with the patient in real life and health conditions. Trauma-focused Cognitive Behavioral Therapy is the most appropriate method of dealing with victims of school transportation. The legal aspect includes the signing of a data revision resolution and the status of a minor in the conclusion of legal negotiations. The article proposes the idea of an author's chatbot, also based on Cognitive Behavioral Therapy and psychological first aid. The author's dialogue agent includes three blocks: work on emotional manifestations, psychoeducation and acute stress relief. In total, the algorithm includes five emotional channels - four dysfunctional and single-functional. The severity of emotional disturbances is measured on a five-point scale and a mood log, by detecting which you can track the effectiveness of the chatbot. A chatbot cannot implement all the functions of therapy, but it can help reduce the level of emotional stress.</p>

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Technical Aspects of Developing Chatbots for Medical Applications: Scoping Review (Preprint)

Safi¹,

Abd‐Alrazaq²,

Khalifa³

et al. 2020

Preprint

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BACKGROUND Chatbots are applications that can conduct natural language conversations with users. In the medical field, chatbots have been developed and used to serve different purposes. They provide patients with timely information that can be critical in some scenarios, such as access to mental health resources. Since the development of the first chatbot, ELIZA, in the late 1960s, much effort has followed to produce chatbots for various health purposes developed in different ways. OBJECTIVE This study aimed to explore the technical aspects and development methodologies associated with chatbots used in the medical field to explain the best methods of development and support chatbot development researchers on their future work. METHODS We searched for relevant articles in 8 literature databases (IEEE, ACM, Springer, ScienceDirect, Embase, MEDLINE, PsycINFO, and Google Scholar). We also performed forward and backward reference checking of the selected articles. Study selection was performed by one reviewer, and 50% of the selected studies were randomly checked by a second reviewer. A narrative approach was used for result synthesis. Chatbots were classified based on the different technical aspects of their development. The main chatbot components were identified in addition to the different techniques for implementing each module. RESULTS The original search returned 2481 publications, of which we identified 45 studies that matched our inclusion and exclusion criteria. The most common language of communication between users and chatbots was English (n=23). We identified 4 main modules: text understanding module, dialog management module, database layer, and text generation module. The most common technique for developing text understanding and dialogue management is the pattern matching method (n=18 and n=25, respectively). The most common text generation is fixed output (n=36). Very few studies relied on generating original output. Most studies kept a medical knowledge base to be used by the chatbot for different purposes throughout the conversations. A few studies kept conversation scripts and collected user data and previous conversations. CONCLUSIONS Many chatbots have been developed for medical use, at an increasing rate. There is a recent, apparent shift in adopting machine learning–based approaches for developing chatbot systems. Further research can be conducted to link clinical outcomes to different chatbot development techniques and technical characteristics.

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Intelligent Counselling Bot Using Ranking Algorithm in AI

Cited by 3 publications

References 7 publications

Technical Aspects of Developing Chatbots for Medical Applications: Scoping Review

Technical Aspects of Developing Chatbots for Medical Applications: Scoping Review

Chat-Bot as a Way to Support Victims of School Bullying

Technical Aspects of Developing Chatbots for Medical Applications: Scoping Review (Preprint)

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