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

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