2 research outputs found
A Neural Topic-Attention Model for Medical Term Abbreviation Disambiguation
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
Zero-Shot Clinical Acronym Expansion via Latent Meaning Cells
We introduce Latent Meaning Cells, a deep latent variable model which learns
contextualized representations of words by combining local lexical context and
metadata. Metadata can refer to granular context, such as section type, or to
more global context, such as unique document ids. Reliance on metadata for
contextualized representation learning is apropos in the clinical domain where
text is semi-structured and expresses high variation in topics. We evaluate the
LMC model on the task of zero-shot clinical acronym expansion across three
datasets. The LMC significantly outperforms a diverse set of baselines at a
fraction of the pre-training cost and learns clinically coherent
representations. We demonstrate that not only is metadata itself very helpful
for the task, but that the LMC inference algorithm provides an additional large
benefit.Comment: To appear in Proceedings Track for Machine Learning for Health (ML4H)
Workshop at NeurIPS (2020