Automatic Extraction of Layman Names for Technical Medical Terms

Medical and health information is widespread in the modern society in light of pressing health concerns and of maintaining of healthy lifestyles. It is also available through modern media (scientific research, medical blogs, clinical documents, TV and radio broadcast, novels, etc.) However, medical area conveys very specific and often opaque notions (eg, myocardial infarction, cholecystectomy, abdominal strangulated hernia, galactose urine), which are difficult to understand by people without medical training. We propose an automatic method for the acquisition of paraphrases for technical medical terms. We expect that the paraphrases are easier to understand than the original terms. The method is based on the morphological analysis of terms and on text mining of social media texts. Analysis of the results and their evaluation indicate that such paraphrases can indeed be found in non specialized documents and show easier understanding level. Depending on the semantics of the terms, the precision values of the extractions ranges between 6 and 100%. This kind of resources is useful for several Natural Language Processing applications (i.e., information retrieval and extraction, text simplification and health literacy, question and answering).