Document Type
Article
Publication Date
1-12-2022
Publication Title
IEEE Journal of Biomedical and Health Informatics
Abstract
Recent applications ofdeep learning have shown promising results for classifying unstructured text in the healthcare domain. However, the reliability of models in production settings has been hindered by imbalanced data sets in which a small subset of the classes dominate. In the absence of adequate training data, rare classes necessitate additional model constraints for robust performance. Here, we present a strategy for incorporating short sequences of text (i.e. keywords) into training to boost model accuracy on rare classes. In our approach, we assemble a set of keywords, including short phrases, associated with each class. The keywords are then used as additional data during each batch of model training, resulting in a training loss that has contributions from both raw data and keywords. We evaluate our approach on classification of cancer pathology reports, which shows a substantial increase in model performance for rare classes. Furthermore, we analyze the impact of keywords on model output probabilities for bigrams, providing a straightforward method to identify model difficulties for limited training data.
First Page
2796
Last Page
2803
PubMed ID
35020599
Volume
26
Issue
6
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Blanchard, Andrew E.; Gao, Shang; Yoon, Hong Jun; Christian, J. Blair; Durbin, Eric B.; Wu, Xiao Cheng; Stroup, Antoinette; Doherty, Jennifer; Schwartz, Stephen M.; Wiggins, Charles; Coyle, Linda; Penberthy, Lynne; and Tourassi, Georgia D., "A Keyword-enhanced Approach To Handle Class Imbalance In Clinical Text Classification" (2022). School of Public Health Faculty Publications. 131.
https://digitalscholar.lsuhsc.edu/soph_facpubs/131
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File Size
3273 KB