The application provides a kind of intelligent structured
medical record generation method and
system based on multimodal doctor-patient interaction, which comprises: real-time acquisition of dialogue voice and transcription into text sequence, while recognizing
visual attention entity by listening to mouse operation in
electronic medical record system.Based on the history of
visual attention entity and text sequence, a logical demonstration track is constructed, and an implicit reward function is derived from it using a reverse
reinforcement learning algorithm. Use the function to calculate the action
reward value of each combination of
visual attention entity and dialogue text, select the highest value combination as the
optimal alignment strategy to determine the timing causal relationship. Map the entity and text to the
medical knowledge graph, extract the shortest semantic path as the implicit clinical reasoning chain, and generate structured
electronic medical record. The application deduces the diagnosis and treatment decision logic from the doctor's multimodal behavior through reverse
reinforcement learning, solving the technical problem that traditional methods cannot establish the internal causal relationship between the doctor's visual
attention focus and spoken content.