Abstract: Clinical findings recognition and Yin & Yang status inference are import tasks in the field of intelligent healthcare.The goal is to identify clinical findings such as diseases and symptoms from doctor-patient dialogue record,then determine their Yin & Yang status.The main weakness of existing research is as follow:(1)Lack of modeling of semantic information and dialogue structure in doctor-patient dialogues,leading to low model accuracy.(2)Implementing it as a two-stage process,it will cause error accumulation.This paper proposes a unified generative method that incorporates dialogue information.It achieves this by constructing a static-dynamic fusion graph to model semantic and structural information in doctor-patient dialogues,enhancing the model's understanding of conversations.And utilizes a generative language model to unify clinical findings recognition andYin & Yang status inference into a sequence generation task,mitigating the problem of error accumulation.Additionally,it improves the accuracy of Yin & Yang statue inference by identifying Yin & Yang statue indicator words.Experimental results on the CHIP2021 evaluation dataset CHIP-MDCFNPC show that the proposed method achieves an F1 score of 71.83%,which is 2.82% higher than the baseline model on average.

Key words: Doctor-Patient dialogue, Clinical findings recognition, Yin & Yang statue inference, Dialogue modeling, Unified generative model