A Keyword-Enhanced Approach to Handle Class Imbalance in Clinical Text Classification

Details

• Alternative Title:
  IEEE J Biomed Health Inform
• Personal Author:
  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 ; Tourassi, Georgia D.
• Description:
  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.
• Subjects:
  Data Collection Humans Reproducibility Of Results
• Keywords:
  Machine Learning Medical Information Systems Natural Language Processing
• Source:
  IEEE J Biomed Health Inform. 26(6):2796-2803
• Pubmed ID:
  35020599
• Pubmed Central ID:
  PMC9533247
• Document Type:
  Journal Article
• Funding:
  HHSN261201800013C/CA/NCI NIH HHSUnited States/ ; HHSN261201800016I/CA/NCI NIH HHSUnited States/ ; HHSN261201800014I/CA/NCI NIH HHSUnited States/ ; U58 DP003907/DP/NCCDPHP CDC HHSUnited States/ ; HHSN261201800013I/CA/NCI NIH HHSUnited States/ ; HHSN261201800007C/CA/NCI NIH HHSUnited States/ ; HHSN261201800014C/CA/NCI NIH HHSUnited States/ ; HHSN261201800016C/CA/NCI NIH HHSUnited States/ ; P30 CA177558/CA/NCI NIH HHSUnited States/ ; HHSN261201300021C/CA/NCI NIH HHSUnited States/
• Volume:
  26
• Issue:
  6
• Collection(s):
  CDC Public Access
• Main Document Checksum:
  urn:sha-512:687a780b2beefd69e4d9db76a24efb66c3c10bd6dfd2443615484f492046ff7963664d43f0ee01b81055a1466fcf2ef1283f2e0be88dbfa9134dd1608be80f03
• Download URL:
  https://stacks.cdc.gov/view/cdc/121728/cdc_121728_DS1.pdf
• File Type:
  [PDF - 1.03 MB ]