Fine-tuning ERNIE for chest abnormal imaging signs extraction

TL;DR

This paper introduces EASON, a method that fine-tunes ERNIE with CRF for extracting abnormal chest imaging signs from Chinese reports, addressing data scarcity and improving extraction accuracy.

Contribution

The paper presents a novel transfer learning approach using ERNIE and a tag2relation algorithm for attribute assignment in chest report information extraction.

Findings

Significant improvement over baseline methods in extraction accuracy

Effective handling of data insufficiency with transfer learning

Robust attribute assignment for abnormal signs

Abstract

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.