• Computer Science
  • Published in ArXiv 2019

A Neural Topic-Attention Model for Medical Term Abbreviation Disambiguation

@article{Li2019ANT,
  title={A Neural Topic-Attention Model for Medical Term Abbreviation Disambiguation},
  author={Irene Li and Michihiro Yasunaga and Muhammed Yavuz Nuzumlali and Cesar Caraballo and Shiwani Mahajan and Harlan M. Krumholz and Dragomir R. Radev},
  journal={ArXiv},
  year={2019},
  volume={abs/1910.14076}
}
Automated analysis of clinical notes is attracting increasing attention. However, there has not been much work on medical term abbreviation disambiguation. Such abbreviations are abundant, and highly ambiguous, in clinical documents. One of the main obstacles is the lack of large scale, balance labeled data sets. To address the issue, we propose a few-shot learning approach to take advantage of limited labeled data. Specifically, a neural topic-attention model is applied to learn improved… CONTINUE READING

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