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Lecture Notes in Computer Science
We describe a first experiment on automated activity and relation identification, and more in general, on the automated identification and extraction of computer-interpretable guideline fragments from clinical documents. We rely on clinical entity and relation (activities, actors, artifacts and their relations) recognition techniques and use MetaMap and the UMLS Metathesaurus to provide lexical information. In particular, we study the impact of clinical document syntax and semantics on thedoi:10.1007/978-3-319-03524-6_20 fatcat:z3jg6vlrtnhpzbugh5xtsmtauq