Fine-tuning ERNIE for chest abnormal imaging signs extraction
This work addresses a domain-specific problem for clinical research and downstream medical tasks by improving extraction from Chinese chest imaging reports, but it is incremental as it adapts existing methods to a new language and data type.
The paper tackled the problem of automatically extracting abnormal imaging signs from Chinese chest imaging reports, which is understudied, by formulating it as a sequence tagging and matching task and proposing EASON, a method based on fine-tuning ERNIE with CRF, to address data insufficiency; the method achieved significant and consistent improvement over baselines on a corpus from a medical big data company.
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.