From Witch’s Shot to Music Making Bones - Resources for Medical Laymen to Technical Language and Vice Versa

Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020), pages 6185–6192 Marseille, 11–16 May 2020 c⃝ European Language Resources Association (ELRA), licensed under CC-BY-NC 6185 From Witch’s Shot to Music Making Bones – Resources for Medical Laymen to Technical Language and Vice Versa Laura Seiffe, Oliver Marten, Michael Mikhailov, Sven Schmeier, Sebastian M¨oller, Roland Roller German Research Center for Artificial Intelligence (DFKI), Speech and Language Technology Lab, Berlin, Germany firstname.secondname@dfki.de Abstract Many people share information in social media or forums, like food they eat, sports activities they do or events which have been visited. This also applies to information about a person’s health status. Information we share online unveils directly or indirectly information about our lifestyle and health situation and thus provides a valuable data resource. If we can make advantage of that data, applications can be created that enable e.g. the detection of possible risk factors of diseases or adverse drug reactions of medications. However, as most people are not medical experts, language used might be more descriptive rather than the precise medical expression as medics do. To detect and use those relevant information, laymen language has to be translated and/or linked to the corresponding medical concept. This work presents baseline data sources in order to address this challenge for German. We introduce a new data set which annotates medical laymen and technical expressions in a patient forum, along with a set of medical synonyms and definitions, and present first baseline results on the data. Keywords: medical laymen to technical language, text simplification, concept normalization 1. Introduction Every day people generate and share information online which sheds light on our lifestyle and also to a certain extent to the health situation. Provided information might include data about sports activities, food, alcohol and drug intake, but also indirectly about potential risk factors of diseases or possible adverse drug reactions, see e.g. Abbar et al. (2015) or Weissenbacher et al. (2018). Mining for instance adverse drug reactions has a high relevance for the general public as well as for pharmacological companies. As the level of medication intake is generally increasing all over the world, so does the risk of unwanted side effects (Kara- petiantz et al., 2018). In most cases, models to extract health related information from text are trained on large annotated data sets, mainly in English language, and on well formed sentences. Text in social media, forums, but also in emails, can differ in terms of sentence structure, writing style and word usage in com- parison to news articles or scientific publications. Think- ing particularly of health related information, the language used might be more casual and descriptive rather than the precise medical expression, as most people are not medi- cal experts. This makes it difficult to identify the precise technical expression and to link it against a unique concept in a biomedical ontology, in order to e.g. gather further background knowledge. This makes it difficult to identify the precise technical expression and to link it …

Content truncated to 500 words