Process fragment recognition in clinical documents

Camilo Thorne, Elena Cardillo, Claudio Eccher, Marco Montali, Diego Calvanese

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations

Abstract

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.

Original languageEnglish
Title of host publicationAI*IA 2013
Subtitle of host publicationAdvances in Artificial Intelligence - XIIIth International Conference of the Italian Association for Artificial Intelligence, Proceedings
Pages227-238
Number of pages12
DOIs
StatePublished - 2013
Externally publishedYes
Event13th International Conference of the Italian Association for Artificial Intelligence, AI*IA 2013 - Turin, Italy
Duration: Dec 4 2013Dec 6 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8249 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference13th International Conference of the Italian Association for Artificial Intelligence, AI*IA 2013
Country/TerritoryItaly
CityTurin
Period12/4/1312/6/13

Keywords

  • Clinical entity and relation recognition
  • Natural language processing
  • Process fragment recognition
  • UMLS Metathesaurus

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