Unsupervised Abbreviation Expansion in Clinical Narratives.

Clinical narratives are typically produced under time pressure, which incites the use of abbreviations and acronyms. To expand such short forms in a correct way eases text comprehension and further semantic processing. We propose a completely unsupervised and data-driven algorithm for the resolution of non-lexicalised and potentially ambiguous abbreviations. Based on the lookup of word bigrams and unigrams extracted from a corpus of 30,000 pseudonymised cardiology reports in German, our method achieved an F<inf>1</inf> score of 0.91, evaluated with a test set of 200 text excerpts. The results are statistically significantly better (p &lt; 0.001) than a baseline approach and show that a simple and domain-independent strategy may be enough to resolve abbreviations when a large corpus of similar texts is available. Further work is needed to combine this strategy with sentence and abbreviation detection modules, to adapt it to acronym resolution and to evaluate it with different datasets.