Process Fragment Recognition in Clinical Documents

Proc. of the 13th Int. Conf. of the Italian Assoc. for Artificial Intelligence (AI*IA 2013). Volume 8249 of Lecture Notes in Computer Science. 2013.

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 the precision of activity and temporal relation recognition.

@inproceedings{AIxIA-2013,
   title = "Process Fragment Recognition in Clinical Documents",
   year = "2013",
  author = "Camilo Thorne and Elena Cardillo and Claudio Eccher and Marco
Montali and Diego Calvanese",
   booktitle = "Proc. of the 13th Int. Conf. of the Italian Assoc. for Artificial
Intelligence (AI*IA 2013)",
   pages = "227--238",
   volume = "8249",
   publisher = "Springer",
   series = "Lecture Notes in Computer Science",
   doi = "10.1007/978-3-319-03524-6_20",
}