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

By Yue Liu, Tao Ge, K. Mathews, Heng Ji, Deborah L. McGuinness. 2015

58 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Molecular Biology

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Citations in the corpus

Unsupervised ensemble ranking of terms in electronic health record notes based on their importance to patients (2017, background): “recognition [45,46], adverse drug event detection [47,48], ranking biomedical synonyms [49], and disambiguating clinical abbreviations [50,51].”
Deep EHR: A Survey of Recent Advances in Deep Learning Techniques for Electronic Health Record (EHR) Analysis (2017, background): “The ﬁrst step to dealing with clinical free text is the extraction of structure, and indeed we have seen several deep learning-based advances in the realm of information extraction from clinical notes [15], [16], [34], [35], [38], [39].”