# Exploiting Task-Oriented Resources to Learn Word Embeddings for Clinical Abbreviation Expansion

arXiv: https://arxiv.org/abs/1804.04225 · HTML: https://mlsanity.com/paper/1804.04225/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Apr 2018.

## Built on by (most cited first, 2 of 2)

- [Graph-Text Multi-Modal Pre-training for Medical Representation Learning](https://mlsanity.com/paper/2203.09994/) (2022)
- [A Biomedical Entity Extraction Pipeline for Oncology Health Records in Portuguese](https://mlsanity.com/paper/2304.08999/) (2023)

## Abstract (arXiv)

In the medical domain, identifying and expanding abbreviations in clinical texts is a vital task for both better human and machine understanding. It is a challenging task because many abbreviations are ambiguous especially for intensive care medicine texts, in which phrase abbreviations are frequently used. Besides the fact that there is no universal dictionary of clinical abbreviations and no universal rules for abbreviation writing, such texts are difficult to acquire, expensive to annotate and even sometimes, confusing to domain experts. This paper proposes a novel and effective approach - exploiting task-oriented resources to learn word embeddings for expanding abbreviations in clinical notes. We achieved 82.27% accuracy, close to expert human performance.