Are my answers medically accurate? Exploiting medical knowledge graphs for medical question answering

Poor health is one of the fundamental causes behind the suffering and deprivation of human beings. One of the United Nations (UN) Sustainable Development Goals is to enhance the quality of healthcare for everyone, which includes economic coverage, availability of high-quality fundamental health-care services, and access to proper, efficient, high-quality, and affordable important vaccinations and medications. Question-Answering (QA) in the medical domain has recently piqued the interest among the researchers and other stakeholders. Medical QA systems have the potential to enhance access to healthcare services, improve patient interactions with doctors, and reduce medical costs through e-medicine. In this paper, we describe a knowledge enabled QA model, which demonstrates how large-scale medical information in the form of knowledge graphs can aid in extracting more relevant answers. The proposed model employs two scoring methods, viz.Medical Entity Scoring (MES) and Context Relevance Scoring (CRS). MES ranks the medical entities from graphs according to their relevance, while CRS is used to reason over the supporting paragraph using the query vector. The system's knowledge is obtained through the use of two distinct resources, viz. PharmKG is used for pharmaceutical terminology management, whereas Unified Medical Language System UMLS is used for general medical terminology management. Empirical results on the MASH-QA and COVID-QA datasets demonstrate that our proposed approach outperforms existing State-of-the-art in both machine evaluation and human judgment.