Exploiting Task-Oriented Resources to Learn Word Embeddings for Clinical Abbreviation Expansion
This work addresses a critical challenge in medical informatics for clinicians and researchers by improving abbreviation expansion in clinical notes, though it is incremental as it builds on existing embedding methods.
The paper tackled the problem of expanding ambiguous abbreviations in clinical texts, particularly in intensive care medicine, by proposing a novel approach that exploits task-oriented resources to learn word embeddings, achieving 82.27% accuracy, which is close to expert human performance.
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.