临床术语标准化即对于医生书写的任一术语,给出其在标准术语集合内对应的标准词。标准词数量多且相似度高,存在Zero-shot和Few-shot等问题,给术语标准化带来了巨大的挑战。该文基于“中国健康信息处理大会”CHIP 2019评测1中提供的数据集,设计并实现了基于BERT蕴含分数排序的临床术语标准化系统。该系统由数据预处理、BERT蕴含打分、BERT数量预测、基于逻辑回归的重排序四个模块组成。用精确率(Accuracy)作为评价指标,最终结果为0.948 25,取得了评测1第一名的成绩。

The normalization of clinical terms is to assign a corresponding term in the standard term set to any term written by the doctor. This task is challenged by large amount of standard terms with high mutual similarity, as well as insufficient training data known as Zero-shot or Few-shot learning. This paper designs and implements a clinical term normalization system based on BERT entailment ranking. The system consists of four modules: data preprocessing, BERT entailment scoring, BERT quantity prediction, and logistic regression-based reordering.Tested in CHIP 2019 Track 1 "Evaluation of Chinese Clinical Term Normalization", it achieves a final accuracy of 0.948 25 as the top score in this campaign.