Log In Sign Up

A Neural Topic-Attention Model for Medical Term Abbreviation Disambiguation

by   Irene Li, et al.

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 contextualized sentence representations for medical term abbreviation disambiguation. Another vital issue is that the existing scarce annotations are noisy and missing. We re-examine and correct an existing dataset for training and collect a test set to evaluate the models fairly especially for rare senses. We train our model on the training set which contains 30 abbreviation terms as categories (on average, 479 samples and 3.24 classes in each term) selected from a public abbreviation disambiguation dataset, and then test on a manually-created balanced dataset (each class in each term has 15 samples). We show that enhancing the sentence representation with topic information improves the performance on small-scale unbalanced training datasets by a large margin, compared to a number of baseline models.


page 1

page 2

page 3

page 4


Learning with less data via Weakly Labeled Patch Classification in Digital Pathology

In Digital Pathology (DP), labeled data is generally very scarce due to ...

Training without training data: Improving the generalizability of automated medical abbreviation disambiguation

Abbreviation disambiguation is important for automated clinical note pro...

Self-Training with Improved Regularization for Few-Shot Chest X-Ray Classification

Automated diagnostic assistants in healthcare necessitate accurate AI mo...

Few-shot Learning for Topic Modeling

Topic models have been successfully used for analyzing text documents. H...

Train Once, Test Anywhere: Zero-Shot Learning for Text Classification

Zero-shot Learners are models capable of predicting unseen classes. In t...

Which images to label for few-shot medical landmark detection?

The success of deep learning methods relies on the availability of well-...

U-Net Convolutional Network for Recognition of Vessels and Materials in Chemistry Lab

Convolutional networks have been widely applied for computer vision syst...