JMIR Publications

ABSTRACT

Background:

In recent years, with the increase in the amount of information and the importance of information screening, increasing attention has been paid to the calculation of textual semantic similarity. In the medical field, with the rapid increase in electronic medical data, electronic medical records and medical research documents have become important data resources for medical clinical research. Medical textual semantic similarity calculation has become an urgent problem to be solved. The 2019 N2C2/OHNLP shared task Track on Clinical Semantic Textual Similarity is one of significant tasks for medical textual semantic similarity calculation.

Objective:

This research aims to solve two problems: 1) The size of medical datasets is small, which leads to the problem of insufficient learning with understanding of the models; 2) The data information will be lost in the process of long-distance propagation, which causes the models to be unable to grasp key information.

Methods:

This paper introduces a text data augmentation method and self-integrated ALBERT model under unsupervised learning to perform clinical textual semantic similarity calculation.

Results:

Compared with the competition methods the 2019 N2C2/OHNLP Track 1 ClinicalSTS , our method achieves state-of-the-art result with a value 0.92 of the Pearson correlation coefficient and surpasses the best result by 2%.

Conclusions:

When the size of medical dataset is small, data augmentation and improved semi-supervised learning can increase the size of dataset and boost the learning efficiency of the model. Additionally, self-ensemble improves the model performance significantly.