Contextual Chatbot for Healthcare Purposes (using Deep Learning)

Kandpal, Prathamesh; Jasnani, Kapil; Raut, Ritesh; Bhorge, Siddharth

doi:10.1109/worlds450073.2020.9210351

Cited by 43 publications

(14 citation statements)

References 1 publication

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“…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. However, these applications can be only operated in the English language.…”

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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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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“…The classification of the NLP models identifying and co-relating to the advanced dialog methodology will be suitable at a faster and easy pace which will in return bring the best results [9]. Suitable recommendation as per the plans to be introduced will not be clashed so that the visibility and transparency of the information required will be stabilized with the use of the required recommendations only when required [10]. In order to understand how important healthcare is for people and especially at an elder stage it is important to impart and make this a daily/weekly practice to self asses to stay fit and well [11].…”

Section: Fig3 Total Messages Coverage-trendmentioning

confidence: 99%

A Diabetic Diet Suggester and Appointment Scheduler Chatbot using Artificial Intelligence and Cloud

Kolanu¹,

Dutta²,

Roy³

et al. 2021

IRJASH

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Conversational Artificial Intelligence has gained a lot of traction due to its ability to communicate with the user and the machine with automated texting and dialog flow based applications that allow computers and humans to communicate in a human-like manner. Hence leveraging this technology, it is able to help guide diabetic patients when they are suffering from diabetes or just to self assess and to make things easier for the people which will allow them to learn and follow a healthy diabetic diet. The usage of Conversational AI-bot will Identify and give answers and analyzes the queries asked that is available based on the text trained. It also allows us to automate book and assist the receptionist to book appointments without any errors This makes things easier and enabling a faster appointment service and this bot helps people to be cautious and lead a healthy life.

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“…Jovanović et al [10] tried to provide the goals of healthcare chatbots by characterizing in service provisioning and highlighting design aspects that require the community's attention, emphasizing the human-AI interaction aspects and the transparency in AI automation and decision making. As for those services AI is applied in the form of digital healthcare including chatbots [11][12][13]. Wu et al [11] summarized the latest developments related to telemedicine and discussed the obstacles and challenges to its wide adoption with a focus on the impact of COVID-19.…”

Section: Introductionmentioning

confidence: 99%

A Study of BERT-Based Classification Performance of Text-Based Health Counseling Data

Sung¹,

Park²,

Kim³

2023

Computer Modeling in Engineering &Amp; Sciences

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The entry into a hyper-connected society increases the generalization of communication using SNS. Therefore, research to analyze big data accumulated in SNS and extract meaningful information is being conducted in various fields. In particular, with the recent development of Deep Learning, the performance is rapidly improving by applying it to the field of Natural Language Processing, which is a language understanding technology to obtain accurate contextual information. In this paper, when a chatbot system is applied to the healthcare domain for counseling about diseases, the performance of NLP integrated with machine learning for the accurate classification of medical subjects from text-based health counseling data becomes important. Among the various algorithms, the performance of Bidirectional Encoder Representations from Transformers was compared with other algorithms of CNN, RNN, LSTM, and GRU. For this purpose, the health counseling data of Naver Q&A service were crawled as a dataset. KoBERT was used to classify medical subjects according to symptoms and the accuracy of classification results was measured. The simulation results show that KoBERT model performed high performance by more than 5% and close to 18% as large as the smallest.

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Contextual Chatbot for Healthcare Purposes (using Deep Learning)

Cited by 43 publications

References 1 publication

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

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

A Diabetic Diet Suggester and Appointment Scheduler Chatbot using Artificial Intelligence and Cloud

A Study of BERT-Based Classification Performance of Text-Based Health Counseling Data

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