Semantic Question Answering: Deep Learning and NoSQL Solution for the Medical Domain

Medical question-answering systems have emerged as innovative tools for healthcare professionals and knowledge seekers. In this work, we leverage advanced Natural Language Processing (NLP) techniques to enhance the capabilities of such systems. Our approach involves the integration of an ontology layer to provide formal structures for medical data, resulting in improved answers through semantic concepts. Additionally, we harness the power of a vector database, reducing system training and response times and incorporate specialized medical deep learning model (BIOBERT) further which elevates the system’s performance. Our work contributes to the domain of medical question-answering by showcasing the potential of advanced NLP techniques and structured medical knowledge. With the usage of the following components in our proposed system: question analysis, answer creation, answer formulation, an ontology layer, a vector database and a session database, this work not only improves healthcare knowledge access but also enhances medical decision-making processes. Our contributions have led to remarkable improvements in accuracy, precision, recall, F score, and mean reciprocal rank (MRR).