Scalable Conversational Intelligence for Post-COVID Health Consultation Using Flask Library and Support Vector Machine

Uday Kumar Adusumilli, Druthi R, Malvika K, Megha N B, R Geetha
2021 International Journal of Scientific Research in Science Engineering and Technology  
In this paper, we put forth a model for natural language processing (NLP)-based Multilingual Conversational Bot in order to provide economic primary healthcare education, information, and advice to chronic patients. Telemedicine and Intelligent Communication Systems can be employed by Medical Practitioners, at scale, as part of their communication strategy during the recent Coronavirus outbreak in order to discourage contagion in the post-covid era. Such systems have the potential to propagate
more » ... ital, valid, authenticated, up to date, information, and support, straight to the masses. The prime intention of the proposed system is to enable our already strained medical infrastructure to scale up its serviceability exponentially. Deep Learning based Conversational Intelligence Systems offer the potential for rapid and radically transforming patient care from an in-person to a remote experience. We introduce a novel computer application that enables healthcare professionals to delegate certain functions. The system was developed precisely, and it has been exhaustively trained to interact with patients alike human beings. The proposed system is based on a serverless architecture, aggregating information from a healthcare professional and providing ideas on preventative measures, home remedies, interactive counseling sessions, healthcare tips, and symptom information on diseases prevalent in rural areas of India. We leverage the resources of the Google Cloud Platform (GCP) for the provision of the said services in India, which will increase the availability of healthcare information to patients, and to leverage the immense potential of AI to eliminate the gap between the demand for healthcare services and the supply of healthcare providers.
doi:10.32628/ijsrset219431 fatcat:cspcozgndnhwvno7xezlvaf7dy