The automatic detection of scientific terms in patient information

Despite the legislative efforts to improve the readability of patient information, different surveys have shown that respondents still feel distressed by reading the information, or even consider it as fully incomprehensible. This paper deals with one of the sources of distress: the use of scientific terminology in patient information. In order to assess the scale of the problem, we collected a Dutch-English parallel corpus of European Public Assessment Reports (EPARs) which was annotated by 2 annotators. This corpus was used for evaluating and training an automatic approach to scientific term detection. We investigated the use of a lexicon-based and a learningbased approach which only relies on text-internal clues. Finally, both approaches were combined in an optimized hybrid learning-based term extraction experiment. We show that whereas the lexicon-based approach yields high precision scores on the detection of scientific terms, its coverage remains limited. The learning-based approach on the other hand demonstrates an Fscore of 80% and remains quite robust despite the highly skewed data set.