The invention discloses a dialogue type
disease diagnosis method based on large
language model feedback, the method combines a
reinforcement learning module and an expert feedback module, the expert feedback module comprises a dynamic symptom attention mechanism, interaction
record management and Prompt construction, the dynamic symptom attention mechanism dynamically distributes weight for each symptom, and the interaction
record management is performed on the interaction
record management and Prompt construction; the model is helped to focus on key symptoms, so that diagnosis accuracy is improved, interaction record management uses a
historical record retrieval method to extract 10 most similar records from the
interaction history of a user, the decision basis of the
reinforcement learning model is further enhanced, Prompt construction is combined with
system prompt, example prompt and current prompt, and the accuracy of diagnosis is improved. The large
language model is guided to generate accurate
score feedback in medical reasoning, and finally the accuracy and stability of a
disease diagnosis
system are improved. The method can effectively cope with challenges of scarcity symptoms and complex symptom combinations, help the model focus on key symptoms, and improve symptom query strategies.