  Chest imaging reports describe the results of chest radiography procedures.
Automatic extraction of abnormal imaging signs from chest imaging reports has a
pivotal role in clinical research and a wide range of downstream medical tasks.
However, there are few studies on information extraction from Chinese chest
imaging reports. In this paper, we formulate chest abnormal imaging sign
extraction as a sequence tagging and matching problem. On this basis, we
propose a transferred abnormal imaging signs extractor with pretrained ERNIE as
the backbone, named EASON (fine-tuning ERNIE with CRF for Abnormal Signs
ExtractiON), which can address the problem of data insufficiency. In addition,
to assign the attributes (the body part and degree) to corresponding abnormal
imaging signs from the results of the sequence tagging model, we design a
simple but effective tag2relation algorithm based on the nature of chest
imaging report text. We evaluate our method on the corpus provided by a medical
big data company, and the experimental results demonstrate that our method
achieves significant and consistent improvement compared to other baselines.
