论文标题
端到端临床事件从中国电子健康记录中提取
End-to-end Clinical Event Extraction from Chinese Electronic Health Record
论文作者
论文摘要
事件提取是医学文本处理的重要工作。根据医学文本注释的复杂特征，我们使用端到端事件提取模型来增强事件的输出格式信息。通过预训练和微调，我们可以提取医学文本四个维度的属性：解剖位置，主题单词，描述单词和出现状态。在测试集中，准确率为0.4511，召回率为0.3928，F1值为0.42。该模型的方法很简单，并且在第七届中国中国健康信息处理会议（CHIP2021）的中国电子病历中赢得了挖掘临床发现事件（任务2）的任务中的第二名。
Event extraction is an important work of medical text processing. According to the complex characteristics of medical text annotation, we use the end-to-end event extraction model to enhance the output formatting information of events. Through pre training and fine-tuning, we can extract the attributes of the four dimensions of medical text: anatomical position, subject word, description word and occurrence state. On the test set, the accuracy rate was 0.4511, the recall rate was 0.3928, and the F1 value was 0.42. The method of this model is simple, and it has won the second place in the task of mining clinical discovery events (task2) in the Chinese electronic medical record of the seventh China health information processing Conference (chip2021).
