Objective The study sought to develop and evaluate a knowledge-based data augmentation
method to improve the performance of deep learning models for biomedical natural …

Abstract We introduce Latent Meaning Cells, a deep latent variable model which learns
contextualized representations of words by combining local lexical context and metadata …

Modern machine learning (ML) technologies have great promise for automating diverse
clinical and research workflows; however, training them requires extensive hand-labelled …

With the rapid spread of information, abbreviations are used more and more common
because they are convenient. However, the duplication of abbreviations can lead to …

Lack of large quantities of annotated data is a major barrier in developing effective text
mining models of biomedical literature. In this study, we explored weak supervision to …

Abbreviations are short forms of phrases that aid in the communication of long sentences in
texts and are an essential part of the writing process. Abbreviations save a lot of time and …

Abbreviations are often overlooked, since their frequency and acceptance are almost
second nature in everyday communication. Business names, handwritten notes, online …

There are a lot of biomedical knowledge hidden in the massive scientific clinical literature.
These knowledge exist in an unstructured form and is difficult to extract automatically …

Readability of clinical texts in electronic medical records (EMRs) is more significant when
EMRs are shared and required to be understood by both human users and computer …

Abbreviations are ubiquitously used in clinical texts of electronic medical records (EMRs) in
the world due to their record simplification when created under high pressure. However, they …