A Novel Approach for Perceptions of Physician Decision-Making and Latent Topic Refinement in Large Language Model-Enhanced Medical Dialog Generation

The rapid advancement of medical dialog generation (MDG) techniques has enabled medical dialog systems (MDSs) to generate high-quality responses rich in medical expertise by integrating diverse medical information. However, they still encounter several challenges, including generic response generation, lack of semantic precision, and imprecise dialog topic extraction. This study aims to design a novel model to address these challenges simultaneously. Correspondingly, we propose the TRL-HMIE model, which represents transformer reinforcement learning (RL) for heterogeneous medical information extraction. In particular, we incorporate GPT-3 from transformer-related models as the reference language model. Our enhancements focused on three key aspects. First, we developed a conversation-topic classifier to precisely categorize conversation topics, supporting the conversation-topic locator module in generating reliable conversation topics. Second, the model employs a multihead attention mechanism to capture crucial information from the dialog context, facilitating the extraction of key dialog information and enhancing the accuracy of heterogeneous information extraction. Finally, the model integrates RL and a reward fusion mechanism, which, combined with its ability to handle multisource information and long dialog contexts, generates optimized rewards for the TRL-HMIE model, encouraging the production of doctor responses with precise semantics and dialog topics. The experimental results demonstrate that the proposed method achieves a 6.07% improvement over the benchmark model on the MedDG and MedDialog datasets. The experimental results demonstrate that the proposed method achieves a 6.07% improvement over the benchmark model on the MedDG and MedDialog datasets.