Although intended for the “average layman”, both in terms of readability and contents, the current patient information still contains many scientific terms. Different studies have concluded that the use of scientific terminology is one of the factors, which greatly influences the readability of this patient information. The present study deals with the problem of automatic term recognition of overly scientific terminology as a first step towards the replacement of the recognized scientific terms by their popular counterpart. In order to do so, we experimented with two approaches, a dictionary-based approach and a learning-based approach, which is trained on a rich feature vector. The research was conducted on a bilingual corpus of English and Dutch EPARs (European Public Assessment Report). Our results show that we can extract scientific terms with a high accuracy (> 80%, 10% below human performance) for both languages. Furthermore, we show that a lexicon-independent approach, which solely relies on orthographical and morphological information is the most powerful predictor of the scientific character of a given term.
2018. Legislation for patient information leaflets in Iran: Focus on lay-friendliness. Health Promotion Perspectives 8:4 ► pp. 263 ff.
DE CLERCQ, ORPHÉE, VÉRONIQUE HOSTE, BART DESMET, PHILIP VAN OOSTEN, MARTINE DE COCK & LIEVE MACKEN
2014. Using the crowd for readability prediction. Natural Language Engineering 20:3 ► pp. 293 ff.
Marciniak, Małgorzata & Agnieszka Mykowiecka
2014. Terminology extraction from medical texts in Polish. Journal of Biomedical Semantics 5:1 ► pp. 24 ff.
Renahy, Julie, Izabella Thomas, Grégory Chippeaux, Bérenger Germain, Xavier Petiaux, Barbara Rath, Valérie de Grivel, Sylviane Cardey & Dominique A. Vuitton
2011. La «langue contrôlée» et l’informatisation de son utilisation au service de la qualité des textes médicaux et de la sécurité dans le domaine de la santé. In Systèmes d’information pour l’amélioration de la qualité en santé [Informatique et Santé, 1], ► pp. 97 ff.
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