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

TL;DR

This paper presents a novel method that leverages task-specific resources to learn word embeddings, significantly improving abbreviation expansion accuracy in clinical texts, approaching expert-level performance.

Contribution

It introduces a new approach utilizing task-oriented resources for learning embeddings to enhance clinical abbreviation expansion.

Findings

Achieved 82.27% accuracy in abbreviation expansion

Close to expert human performance

Effective use of task-oriented resources

Abstract

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.