KNSE : A Knowledge-aware Natural Language Inference Framework for Dialogue Symptom Status Recognition (2023)

Abstract: Symptom diagnosis in medical conversations aims to correctly extract both symptom entities and their status from the doctor-patient dialogue. In this paper, we propose a novel framework called KNSE for symptom status recognition (SSR), where the SSR is formulated as a natural language inference (NLI) task. For each mentioned symptom in a dialogue window, we first generate knowledge about the symptom and hypothesis about status of the symptom, to form a (premise, knowledge, hypothesis) triplet. The BERT model is then used to encode the triplet, which is further processed by modules including utterance aggregation, self-attention, cross-attention, and GRU to predict the symptom status. Benefiting from the NLI formalization, the proposed framework can encode more informative prior knowledge to better localize and track symptom status, which can effectively improve the performance of symptom status recognition. Preliminary experiments on Chinese medical dialogue datasets show that KNSE outperforms previous competitive baselines and has advantages in cross-disease and cross-symptom scenarios.

## introduction dialogue symptom diagnosis is an important task in medical dialogue modeling, which is widely used in automatic construction of electronic medical records (emrs) @xcite @xcite and automatic diagnosis systems @xcite @xcite . dialogue symptom diagnosis can be defined as two subtasks: symptom entity recognition (ser) and symptom status recognition (ssr). the former aims to identify symptom entities from doctorpatient dialogues, while the latter aims to further clarify the relationship between identified symptoms and patients. ## conclusion in this paper, we investigate the problem of symptom diagnosis in doctor-patient dialogues. we proposed a knowledge-aware framework by formalizing the symptom status recognition problem as a natural language inference task. our framework is able to encode more informative prior knowledge to better localize and track symptom status, which can effectively improve the performance of symptom status recognition. we develop several competitive baselines for comparison and conduct extensive experiments on the cmdd dataset. the experimental results demonstrate the effectiveness of our framework, especially in cross-disease and cross-symptom scenarios.