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In rural regions of several developing countries, access to qualityhealthcare, medical infrastructure, and professional diagnosis is largelyunavailable. Many of these regions are gradually gaining access to internetinfrastructure, although not with a strong enough connection to allow forsustained communication with a medical practitioner. Several deaths resultingfrom this lack of medical access, absence of patient's previous health records,and the unavailability of information in indigenous languages can be easilyprevented. In this paper, we describe an approach leveraging the phenomenalprogress in Machine Learning and NLP (Natural Language Processing) techniquesto design a model that is low-resource, multilingual, and a preliminaryfirst-point-of-contact medical assistant. Our contribution includes definingthe NLP pipeline required for named-entity-recognition, language-agnosticsentence embedding, natural language translation, information retrieval,question answering, and generative pre-training for final query processing. Weobtain promising results for this pipeline and preliminary results for EHR(Electronic Health Record) analysis with text summarization for medicalpractitioners to peruse for their diagnosis. Through this NLP pipeline, we aimto provide preliminary medical information to the user and do not claim tosupplant diagnosis from qualified medical practitioners. Using the input fromsubject matter experts, we have compiled a large corpus to pre-train andfine-tune our BioBERT based NLP model for the specific tasks. We expect recentadvances in NLP architectures, several of which are efficient andprivacy-preserving models, to further the impact of our solution and improve onindividual task performance.

Multilingual Medical Question Answering and Information Retrieval for Rural Health Intelligence Access