结合规则学习与深度学习的诊疗关系抽取

EXTARCTION OF DIAGNOSIS AND TREATMENT RELATIONSHIP BASED ON RULE LEARNING AND DEEP LEARNING

诊疗关系的自动识别和抽取有助于医生进行诊疗决策。传统的关系抽取模型对部分数据没有良好的解释性,因此,以神经网络进行规则学习和泛化,设计打分机制,通过规则匹配实现关系抽取,而后对未正确匹配数据进行针对性深度学习模型训练,完成最终的诊疗关系抽取。使用以疾病为中心的诊疗流程相关文本展开实验验证该方法的效果。实验结果表明,该方法不仅通过少量人工规则使关系抽取增加了可解释性,还可以显著提高关系抽取的效果。

The automatic identification and extraction of diagnosis and treatment relationships helps doctors make diagnosis and treatment decisions.The traditional relationship extraction model does not have good interpretability for part of the data.Therefore,this paper used neural network for rule learning and generalization,designed a scoring mechanism,and achieved relationship extraction through rule matching.A targeted deep learning model for incorrectly matched data training was proceeded to complete the final diagnosis and treatment relationship extraction.The relevant texts of the disease-centric diagnosis and treatment process were used for experiments to verify the effect of this method.The results show that the text method not only increases the interpretability of relationship extraction through a few manual rules,but also significantly improves the effect of relationship extraction.