Improving Layman Readability of Clinical Narratives with Unsupervised Synonym Replacement

Hans Moen, Laura-Maria Peltonen, Mikko Koivumäki, Henry Suhonen, Tapio Salakoski, Filip Ginter, Sanna Salanterä

Abstract: We report on the development and evaluation of a prototype tool aimed to assist laymen/patients in understanding the content of clinical narratives. The tool relies largely on unsupervised machine learning applied to two large corpora of unlabeled text – a clinical corpus and a general domain corpus. A joint semantic word-space model is created for the purpose of extracting easier to understand alternatives for words considered difficult to understand by laymen. Two domain experts evaluate the tool and inter-rater agreement is calculated. When having the tool suggest ten alternatives to each difficult word, it suggests acceptable lay words for 55.51% of them. This and future manual evaluation will serve to further improve performance, where also supervised machine learning will be used.