MedProm: Bridging Dialogue Gaps in Healthcare with Knowledge-Enhanced Generative Models

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In medical dialogue systems, recent advancements underscore the critical role of incorporating relevant medical knowledge to enhance performance. However, existing knowledge bases often lack completeness, posing a challenge in sourcing pertinent information. We present MedProm, a novel generative model tailored for medical dialogue generation to address this gap. Motivated by the need for comprehensive and contextually relevant responses, MedProm leverages state-of-the-art language models such as BioGPT. Our model is designed to integrate extensive medical knowledge into conversations, facilitating effective communication between patients and healthcare providers. At the core of MedProm lies the MediConnect Graph, a meticulously constructed knowledge graph capturing intricate relationships among medical entities extracted from dialogue contexts. By employing a KnowFusion encoder with a pretraining objective and masked multi-head self-attention, MedProm effectively processes the MediConnect graph, enabling precise control over information flow to capture its underlying structure. Furthermore, MedProm incorporates a sophisticated Curriculum Knowledge Decoder, leveraging transformer-based decoding to generate response utterances conditioned on input representations from the KnowFusion Encoder. The training process is guided through curriculum learning, gradually increasing optimization difficulty based on a coherence-based criterion. Experimental results on two datasets demonstrate the efficacy of MedProm in generating accurate and contextually relevant responses compared to state-of-the-art models.