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.

翻译：医学对话中的症状诊断旨在从医患对话中正确提取症状实体及其状态。本文提出一种名为KNSE的新型症状状态识别框架，将症状状态识别形式化为自然语言推理任务。对于对话窗口中的每个提及症状，我们首先生成关于该症状的知识以及关于症状状态的假设，形成（前提，知识，假设）三元组。随后采用BERT模型对该三元组进行编码，并通过话语聚合、自注意力、交叉注意力及GRU等模块进一步处理以预测症状状态。得益于自然语言推理的形式化，该框架能够编码更丰富的先验知识以更好地定位和追踪症状状态，从而有效提升症状状态识别的性能。在中文医学对话数据集上的初步实验表明，KNSE优于以往具有竞争力的基线模型，并在跨疾病及跨症状场景中展现出显著优势。