BGKnow-Medical Chatbot: A Hybrid Approach Based on Knowledge Graph and GPT-2

Accurate and timely diagnosis is critical in ensuring patients receive care and treatment for their medical conditions. Traditional symptom checkers often lack accuracy and efficiency in diagnosing diseases, as they typically rely on preprogrammed decision trees or rule-based algorithms that may not account for the complexity and variability of symptoms. By using natural language processing (NLP) and machine learning techniques, such as knowledge graphs and Bio-Bidirectional Encoder Representations From Transformers (BioBERT), chatbots can provide a more accurate and personalized approach to disease diagnosis. This paper introduces a hybrid chatbot framework called “BGKnow.” BGKnow represents the combination of a knowledge graph and Generative Pre-trained Transformer 2 (GPT-2) model that uses BioBERT embeddings for effective diagnosis based on symptoms entered by users. The proposed system shows promising potential for assisting healthcare professionals in accurately and efficiently addressing medical inquiries.