Automatic diagnosis of understanding of medical words

Within the medical field, very specialized terms are commonly used, while their understanding by laymen is not always successful. We propose to study the understandability of medical words by laymen. Three annotators are involved in the creation of the reference data used for training and testing. The features of the words may be linguistic (i.e., number of characters, syllables, number of morphological bases and affixes) and extra-linguistic (i.e., their presence in a reference lexicon, frequency on a search engine). The automatic categorization results show between 0.806 and 0.947 F-measure values. It appears that several features and their combinations are relevant for the analysis of understandability (i.e., syntactic categories, presence in reference lexica, frequency on the general search engine, final substring).