Automatized Medical Chatbot (Medibot)

Srivastava, Prakhar; Singh, Nishant

doi:10.1109/parc49193.2020.236624

Cited by 58 publications

(9 citation statements)

References 2 publications

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“…Relationship of the results: the critical factors that we proposed are related in the adjacency matrix and are simulated in the cognitive map; Furthermore, the centrality of the map expresses that the implementation of databases and the load of responses to the database is the most critical factor in this simulation; the general architecture is a basic idea for a chatbot. Our research agrees with the proposal of critical factors for software development with the following references: [11,12,[17][18][19][20]] y [21]; chatbots aimed at assistance or support: [3,[5][6][7][8][9]14] y [15].…”

Section: Discussionsupporting

confidence: 90%

“…There are chatbots in the industrial area, homes, autonomous cars, medical support [1], social networks [2]; step to information, debates, learning [3]; education, geographic orientation, personal services [4]; robots, personal assistance, driving assistance [5]; remote assistance for clients, training, reservations [6]; recreation, commercial activities, user support [7]; sales, repetitive questions [8]; all to assist human beings. Some properties of chatbots are text or auditory interaction, use of language processing, keyword searches, word manipulation, applied in small or large companies, it does not increase the use of human resources [7].…”

Section: Introductionmentioning

confidence: 99%

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Chatbot for Technical Support, Analysis of Critical Success Factors Using Fuzzy Cognitive Maps

Martínez

Plua

Rios

et al. 2021

Communications in Computer and Information Science

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Remote assistance needs to be automated for better coverage in time, quality, and quantity of clients. The problem is that by increasing the number of clients with the same amount of attention personnel or none, the waiting time of clients increases, and the quality of attention decreases. The objective of this research is to perform an analysis of the general critical success factors by simulating fuzzy cognitive maps applied to a chatbot for technical support. The methodology applied is exploratory, qualitative, descriptive research and de-duction to analyze the references on chatbots, technical support, critical success factors and fuzzy cognitive maps. This research resulted in a Definition of general critical success factors for a technical support chatbot, a Simulation of critical factors in a fuzzy cognitive map, an Analysis of critical success factors, and a general architecture prototype for the technical support chatbot. It was concluded that among the main critical factors for a project are important elements the knowledge of experts, expertise, and human resources; the application and analysis of CSF through FCM helps in the improvement and optimization of the factors/tasks of the chatbot project for technical support.

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Section: Discussionsupporting

confidence: 90%

Section: Introductionmentioning

confidence: 99%

Chatbot for Technical Support, Analysis of Critical Success Factors Using Fuzzy Cognitive Maps

Martínez

Plua

Rios

et al. 2021

Communications in Computer and Information Science

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“…Main challenges are highlighted which include the utilization of data and integration further added feature will be the efficiency of data. Srivastava and Singh (2020) presented a Medibot technique to identify the user's symptoms with a 65% precision via NLP. Kandpal et al (2020) presented a Contextual chatbot to offer healthcare services using deep learning.…”

Section: Review Of Related Workmentioning

confidence: 99%

An ensemble approach for healthcare application and diagnosis using natural language processing

Alekhya¹,

Sasikumar²

2022

Cogn Neurodyn

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Integration of healthcare records into a single application is still a challenging process There are additional issues when data becomes heterogeneous, and its application based on users does not appear to be the same. Hence, we propose an application called MEDSHARE which is a web-based application that integrates the data from various sources and helps the patient to access all their health records in a single point of source. Apart just from the collection of data, this portal enables the process of diagnosis using Natural language processing. The process is carried out by fuzzy logic ruleset which is generated by using NLP packages. The resulted information is given to the SVM classifier which helps in the prediction of diseases resulting in 89% of accuracy and standing the best compared to other classifiers. Finally, the observations resulted are sent to the front end application and the concerned user mobile through text message in their own native language for which translation package is been used.

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“…Por outro lado, os chatbots vêm evoluindo e conquistando um espac ¸o há muito defasado: o atendimento. Aplicac ¸ões de chatbot vêm sendo difundidas em diferentes áreas do conhecimento, seja para atendimento em lojas online (Bhawiyuga et al 2017), assistentes médicos (Srivastava and Singh 2020) ou ferramenta de educac ¸ão assistiva (da Silveira et al 2019). Um chatbot é capaz de reconhecer uma certa gama de sentenc ¸as presentes em sua base de dados e buscar respostas de forma instantânea para uma grande quantidade de usuários.…”

Section: Introduc ¸ãOunclassified

Chatbot to Support Frequently Asked Questions from Students in Higher Education Institutions

Neto

Miranda

Mello

et al. 2022

Anais Do XIX Encontro Nacional De Inteligência Artificial E Computacional (ENIAC 2022)

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Os chatbots são ferramentas que utilizam inteligência artificial para simular uma conversação humana. Eles podem ser utilizados para diferentes aplicações, como fornecer atendimento dentro de um e-commerce, preenchimento de formulários ou responder a FAQs (Frequently Asked Questions). Este trabalho propõe o desenvolvimento de um chatbot para auxiliar os estudantes de uma universidade pública brasileira na busca de informações relacionadas a dúvidas em disciplinas, processos administrativos, e dúvidas gerais sobre seu curso ou universidade. O sistema desenvolvido consegue entregar uma precisão alta na classificação da intenção da pergunta e ter resposta ao usuário em uma ampla margem de tópicos diferentes.

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Automatized Medical Chatbot (Medibot)

Cited by 58 publications

References 2 publications

Chatbot for Technical Support, Analysis of Critical Success Factors Using Fuzzy Cognitive Maps

Chatbot for Technical Support, Analysis of Critical Success Factors Using Fuzzy Cognitive Maps

An ensemble approach for healthcare application and diagnosis using natural language processing

Chatbot to Support Frequently Asked Questions from Students in Higher Education Institutions

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