基于批数据过采样的中医临床记录四诊描述抽取方法

Four Diagnostic Description Extraction in Clinical Records of Traditional Chinese Medicine with Batch Data Oversampling

中医临床记录四诊描述抽取对中医临床辨证论治的提质增效具有重要的应用价值,然而该任务尚有待探索,类别分布不均衡是该任务面临的关键挑战之一。该文围绕中医临床记录四诊描述抽取任务展开研究,首先构建了中医临床四诊描述抽取语料库;然后基于无标注的中医临床记录微调通用预训练语言模型实现该模型的领域适应;最后利用小规模标注数据,采用批数据过采样算法,完成中医临床记录四诊描述抽取模型的训练。实验结果表明,该文提出的抽取方法的总体性能均优于对比方法,并且与对比方法的最优结果相比,该文方法将少见类别的抽取性能F1值平均提升了2.13%。

Four diagnostic description extraction in clinical records has clinical application in improving the practice of traditional Chinese medicine.As the first exploration of this extraction task,we firstly construct a clinical four diagnostic description extraction corpus and then fine-tune a general domain pre-trained language model based on unlabeled clinical records of traditional Chinese medicine.We train the proposed four diagnostic description extraction model by utilizing a small labeled dataset through a well-designed batch data oversampling algorithm.The experimental results show that the performance of the proposed method in this paper is better than that of the compared methods,with an average improvement of the rare classes by 2.13%F 1 score.