Processing Patient Information Leaflets with Embeddings Stahlmann, Sven Hirschmeier, Stefan Schoder, Detlef 2021-12-24T17:52:08Z 2021-12-24T17:52:08Z 2022-01-04
dc.description.abstract As of 2021, more than 100,000 drugs are approved in Germany, 35,000 of which are non-prescriptive over-the-counter drugs. While proven information from medical studies is given in patient information leaflets, patients are often lost when trying to determine which drugs are compatible with their needs or which alternatives are suitable. We show that representing patient information leaflets as dense vectors allows us to extract more valuable medical information than is explicitly stated in the leaflets. Without any explicit insertion of medical knowledge, our embeddings capture concepts of generics, even with respect to the dosage form. Furthermore, the embeddings allow patients to identify drug clusters based on their treatment area and offer suitable alternatives based on analogical reasoning. The carved-out information may not only help patients to explore alternative drugs but also serve pharmacists and patients as a new way to search for drugs tailored to dietary, allergic, or medical needs.
dc.format.extent 10 pages
dc.identifier.doi 10.24251/HICSS.2022.453
dc.identifier.isbn 978-0-9981331-5-7
dc.language.iso eng
dc.relation.ispartof Proceedings of the 55th Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.subject Decision Support for Healthcare Processes and Services
dc.subject data mining
dc.subject decision support systems
dc.subject embeddings
dc.subject patient information leaflets
dc.title Processing Patient Information Leaflets with Embeddings
dc.type.dcmi text
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