Building a semantically annotated corpus of clinical texts.
In this paper, we describe the construction of a semantically annotated corpus of clinical texts for use in the development and evaluation of systems for automatically extracting clinically significant information from the textual component of patient records. The paper details the sampling of textual material from a collection of 20,000 cancer patient records, the development of a semantic annotation scheme, the annotation methodology, the distribution of annotations in the final corpus, and the use of the corpus for development of an adaptive information extraction system. The resulting corpus is the most richly semantically annotated resource for clinical text processing built to date, whose value has been demonstrated through its use in developing an effective information extraction system. The detailed presentation of our corpus construction and annotation methodology will be of value to others seeking to build high-quality semantically annotated corpora in biomedical domains.
Roberts
Angus
A
Department of Computer Science, University of Sheffield, Regent Court, 211 Portobello, Sheffield S1 4DP, UK. a.roberts@dcs.shef.ac.uk
Gaizauskas
Robert
R
Hepple
Mark
M
Demetriou
George
G
Guo
Yikun
Y
Roberts
Ian
I
Setzer
Andrea
A
D000043:Abstracting and Indexing; D035843:Biomedical Research; D017408:Guidelines as Topic; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods*; D020407:Internet; D008499:Medical Records; D015233:Models, Statistical; D009323:Natural Language Processing; D009369:Neoplasms; D012660:Semantics; D009626:Terminology as Topic; D014584:User-Computer Interface