EPR: Entity Perception and Reasoning for Medical Dialogue System

The medical dialogue system aims to create a smart consultation platform for diagnosing diseases. Prior research uses doctor-patient dialogue history for responses, neglecting medical clues guidance. This oversight can lead to inconsistencies between responses and crucial medical clues in context. To solve this problem, we propose Entity Perception and Reasoning for Medical Dialogue System (EPR), which is built on two components, i.e., Entity-Perception Module and Local Entity Attention Module. Entity-Perception Module first predicts medical entities (e.g., symptoms, diseases, and medicines) included in the next response through dialogue history as explicit clues to simulate the diagnosis process of real doctors, then Local Entity Attention Module detects the corresponding relevance between medical entities in dialogue history and medical entities in the next response as implicit clues to reason internal transferability between medical entities. Finally, EPR aggregates above medical clues and guide dialogue history to achieve the consistency of medical response and contextual reasoning logic. Experimental results show that these methods effectively improve entity-based metrics on MedDG.