Talk2Doc: A Patient Q A system using Retrieval-Augmented Generation with Weighted Knowledge Graphs and LLMs

Effective retrieval in patient question-answering Q A systems is essential for addressing complex medical and healthcare inquiries. Traditional retrieval-augmented generation RAG methods leveraging large language models LLMs treat historical dialogues and issue-tracking tickets as unstructured text, overlooking critical intra- and inter-issue structures and semantic relationships. This limitation often reduces the contextual relevance and accuracy of generated responses. This paper introduces Talk2Doc, a novel Q A system that combines RAG, weighted knowledge graphs KGs , and LLMs to enhance response quality and contextual understanding in healthcare applications. In particular, Talk2Doc constructs a weighted KG from patient questions, preserving both intra-issue structures and inter-issue relationships. By retrieving relevant subgraphs, the system generates precise, contextually aware answers, effectively mitigating the drawbacks of fragmented text representations. The proposed system was rigorously evaluated using standard retrieval metrics NDCG@K, Recall@K, MRR and text generation metrics BLEU, METEOR, ROUGE . Results show that Talk2Doc significantly outperforms existing approaches, improving answer accuracy and maintaining the structural integrity of patient dialogue information. By prioritizing semantic relationships among medical entities, Talk2Doc refines retrieval performance, ensuring high-quality responses. Scalable across diverse medical domains and languages, Talk2Doc represents a transformative advancement for healthcare Q A systems.