Multilingual Healthcare Chatbot Using Machine Learning

Badlani, Sagar; Aditya, Tanvi; Dave, Meet; Chaudhari, Sheetal

doi:10.1109/incet51464.2021.9456304

Cited by 32 publications

(7 citation statements)

References 13 publications

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“…Zhang et al (2017) explore medical query intents. Most of the works are done for English and Chinese languages and there is no proper architecture for Indian multilingual scenarios for intent and entity extraction.. C) Health Care in Indian Languages: Some researchers focus on Indian Languages -Hindi Medical Conversation system, MedBot (Bharti et al, 2020), detecting Hindi and English COVID-19 posts (Patwa et al, 2021), Tamil health information (Santosh, 2019), Bengali health-bot (Badlani et al, 2021), Telugu COVID-19 health information (Vishwas et al, 2017). But none of the work aims at Indian health query datasets and model analysis.…”

Section: Related Workmentioning

confidence: 99%

Intent Identification and Entity Extraction for Healthcare Queries in Indic Languages

Mullick¹,

Mondal²,

Ray³

et al. 2023

Preprint

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Scarcity of data and technological limitations for resource-poor languages in developing countries like India poses a threat to the development of sophisticated NLU systems for healthcare. To assess the current status of various state-of-the-art language models in healthcare, this paper studies the problem by initially proposing two different Healthcare datasets, Indian Healthcare Query Intent-WebMD and 1mg (IHQID-WebMD and IHQID-1mg) and one real world Indian hospital query data in English and multiple Indic languages (Hindi, Bengali, Tamil, Telugu, Marathi and Gujarati) which are annotated with the query intents as well as entities. Our aim is to detect query intents and extract corresponding entities. We perform extensive experiments on a set of models in various realistic settings and explore two scenarios based on the access to English data only (less costly) and access to target language data (more expensive). We analyze context specific practical relevancy through empirical analysis. The results, expressed in terms of overall F1 score show that our approach is practically useful to identify intents and entities.

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Section: Related Workmentioning

confidence: 99%

Intent Identification and Entity Extraction for Healthcare Queries in Indic Languages

Mullick¹,

Mondal²,

Ray³

et al. 2023

Preprint

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show abstract

“…Multilingual chatbots have been developed in domains other than education. For example, [28] proposed a multilingual health chatbot application that can diagnose disease based on user symptoms and supports three languages: English, Hindi, and Gujarati. Reference [29] presents a bilingual retail chatbot that can handle Filipino-Tagalog and English languages that employs k-fold cross-validation on a dataset generated using a bilingual automatic corpus engine.…”

Section: Chatbots In Arabic and Other Languagesmentioning

confidence: 99%

Bilingual AI-Driven Chatbot for Academic Advising

Bilquise¹,

Ibrahim²,

Shaalan³

2022

IJACSA

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Conversational technologies are revolutionizing how organizations communicate with people, thereby raising quick responses and constant availability expectations. Students often have queries about the institutional and academic policies and procedures, academic progression, activities, and more in an academic environment. In reality, the student services team and the academic advisors are overwhelmed with several queries that they cannot provide instant responses to, resulting in dissatisfaction with services. Our study leverages Artificial Intelligence and Natural Language processing technologies to build a bilingual chatbot that interacts with students in the English and Arabic languages. The conversational agent is built in Python and designed for students to support advising-related queries. We use a purpose-built domain-specific corpus consisting of the common questions advisors receive from students and their responses as the chatbots knowledge base. The chatbot engine determines the user intent by processing the input and retrieves the most appropriate response that matches the intent with an accuracy of 80% in English and 75% in Arabic. We also evaluated the chatbot interface by conducting field experiments with students to test the accuracy of the chatbot with real-time input and test the application interface.

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“…Since health in India is a state-specific issue, this situation is not the same throughout the country. [30] A MPL state-space representation that captures the behaviour of repetitive execution systems with a MIMO-FIFO structure was developed in this study. [9] Since the elements for representing precedence restrictions and execution durations are combined in the usual MPL form, all representation matrices must be recalculated whenever the system parameters are altered.…”

Section: Literature Reviewmentioning

confidence: 99%

A Novel System for AYUSH Healthcare Services using Classification and Regression

Kanjalkar¹,

Kanjalkar²,

Deshmukh³

et al. 2022

IJRITCC

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There are roughly 4000 AYUSH hospitals spread out across India under various councils and hospitals run by the Indian government. Today’s atmosphere makes it more challenging than ever to locate a suitable AYUSH facility for the treatment. The AYUSH Ministry provides India’s top option for healthcare delivery. The government is examining strategies to lower expenditures while enhancing patient care. We are proposing the ground-breaking idea of e-healthcare which involves various novel features like suggesting various tools to the patients those need to communicate with the healthcare professionals as per their convenience remotely. This research suggests an interactive system using Android in line with this trend. By integrating different bio-medical data sources that contain information pertinent to the hospital demographics, their inpatient procedure rates, Outpatient department, etc., we proposed a system that surveys on the various AYUSH hospitals to find. This system uses the Google Map API for tracking and highlighting the location to the nearby AYUSH hospitals with opening and closing timings. Additionally, the proposed system plays a crucial role in emergency scenarios by supporting the user in performing the necessary first aid techniques. Using this strategy, the entire system demonstrates that this research provides a superior method for making decisions than past studies.

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Multilingual Healthcare Chatbot Using Machine Learning

Cited by 32 publications

References 13 publications

Intent Identification and Entity Extraction for Healthcare Queries in Indic Languages

Intent Identification and Entity Extraction for Healthcare Queries in Indic Languages

Bilingual AI-Driven Chatbot for Academic Advising

A Novel System for AYUSH Healthcare Services using Classification and Regression

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