Processing Patient Information Leaflets with Embeddings

Stahlmann, Sven
Hirschmeier, Stefan
Schoder, Detlef
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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.
Decision Support for Healthcare Processes and Services, data mining, decision support systems, embeddings, patient information leaflets
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