Florence- A Health Care Chatbot

Gupta, Jahnvi; Singh, Vinay Kumar; Kumar, I J John Bharath

doi:10.1109/icaccs51430.2021.9442006

Cited by 37 publications

(15 citation statements)

References 2 publications

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“…Gupta et al [17] created a chatbot to assist customers in predicting their potential sickness through a simple discussion in which they would be questioned about their symptoms, emotions, and diets. It will inform indi-viduals about the severity of their sickness and whether or not they need to take action to combat it.…”

Section: Methods Detailsmentioning

confidence: 99%

MedLingua: A conceptual framework for a multilingual medical conversational agent

Himel,

Hasan,

Salsabil

et al. 2024

MethodsX

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Section: Methods Detailsmentioning

confidence: 99%

MedLingua: A conceptual framework for a multilingual medical conversational agent

Himel,

Hasan,

Salsabil

et al. 2024

MethodsX

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“…is study also designs mapping rules between common natural language understanding models, which can be used to make chatbot architecture more flexible. Gupta et al [45] propose a novel health care chatbot based on the RASA framework, which can initially predict the disease of the patient and give certain treatment suggestions through dialogue without any hassle. SLU typically involves identifying the intent and extracting semantic constituents from the natural language query, two tasks that are often referred to as intent detection and slot filling [28].…”

Section: Spoken Language Understandingmentioning

confidence: 99%

Learning High-Order Semantic Representation for Intent Classification and Slot Filling on Low-Resource Language via Hypergraph

Qi¹,

Gao²,

Wang³

et al. 2022

Mathematical Problems in Engineering

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Representation of language is the rst and critical task for Natural Language Understanding (NLU) in a dialogue system. Pretraining, embedding model, and ne-tuning for intent classi cation and slot-lling are popular and well-performing approaches but are time consuming and ine cient for low-resource languages. Concretely, the out-of-vocabulary and transferring to di erent languages are two tough challenges for multilingual pretrained and cross-lingual transferring models. Furthermore, quality-proved parallel data are necessary for the current frameworks. Stepping over these challenges, di erent from the existing solutions, we propose a novel approach, the Hypergraph Transfer Encoding Network "HGTransEnNet. e proposed model leverages o -the-shelf high-quality pretrained word embedding models of resource-rich languages to learn the high-order semantic representation of low-resource languages in a transductive clustering manner of hypergraph modeling, which does not need parallel data. e experiments show that the representations learned by "HGTransEnNet" for low-resource language are more e ective than the state-of-the-art language models, which are pretrained on a large-scale multilingual or monolingual corpus, in intent classi cation and slot-lling tasks on Indonesian and English datasets.

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“…Florence [21] chatbot, which functions on Facebook Messenger, Skype, or Kik, is essentially a "personal nurse". She can remind people to take their medication, which could be a useful feature for elderly patients.…”

Section: Related Workmentioning

confidence: 99%

CO-BOT: An Intelligent Technique for Designing a Chatbot for Initial COVID-19 Test

Das

Nath

Banerjee³

et al. 2022

j. of Comput. sci. res.

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The coronavirus (nCOV-19), which was discovered, has now spread around the world. However, managing the flow of a large number of cases has proven to be a significant issue for hospitals or healthcare professionals. It is becoming increasingly challenging to speak with a medical expert after the epidemic’s initial wave has passed, particularly in rural areas. Thus, it becomes clear that a Chatbot that is well-designed and implemented can assist patients who are located far away by advocating preventive actions, and viral updates in various cities, and minimising the psychological harm brought on by dread. In this study, a sophisticated Chabot’s design for diagnosing individuals who have been exposed to COVID-19 is presented, along with recommendations for immediate safety measures. Additionally, when symptoms grow serious, this virtual assistant makes contact with specialised medical professionals.

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Florence- A Health Care Chatbot

Cited by 37 publications

References 2 publications

MedLingua: A conceptual framework for a multilingual medical conversational agent

MedLingua: A conceptual framework for a multilingual medical conversational agent

Learning High-Order Semantic Representation for Intent Classification and Slot Filling on Low-Resource Language via Hypergraph

CO-BOT: An Intelligent Technique for Designing a Chatbot for Initial COVID-19 Test

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