Format

Send to

Choose Destination
BMC Bioinformatics. 2014 Nov 29;15:373. doi: 10.1186/s12859-014-0373-3.

ContextD: an algorithm to identify contextual properties of medical terms in a Dutch clinical corpus.

Author information

1
Department of Medical Informatics, Erasmus Medical Center, P.O. Box 2040, Rotterdam, CA, 3000, Netherlands. m.afzal@erasmusmc.nl.
2
Department of Medical Informatics, Erasmus Medical Center, P.O. Box 2040, Rotterdam, CA, 3000, Netherlands. e.pons@erasmusmc.nl.
3
Department of Medical Informatics, Erasmus Medical Center, P.O. Box 2040, Rotterdam, CA, 3000, Netherlands. n.kang@erasmusmc.nl.
4
Department of Medical Informatics, Erasmus Medical Center, P.O. Box 2040, Rotterdam, CA, 3000, Netherlands. m.sturkenboom@erasmusmc.nl.
5
Janssen Research and Development LLC, Titusville, NJ, USA. mschuemi@its.jnj.com.
6
Department of Medical Informatics, Erasmus Medical Center, P.O. Box 2040, Rotterdam, CA, 3000, Netherlands. j.kors@erasmusmc.nl.

Abstract

BACKGROUND:

In order to extract meaningful information from electronic medical records, such as signs and symptoms, diagnoses, and treatments, it is important to take into account the contextual properties of the identified information: negation, temporality, and experiencer. Most work on automatic identification of these contextual properties has been done on English clinical text. This study presents ContextD, an adaptation of the English ConText algorithm to the Dutch language, and a Dutch clinical corpus. We created a Dutch clinical corpus containing four types of anonymized clinical documents: entries from general practitioners, specialists' letters, radiology reports, and discharge letters. Using a Dutch list of medical terms extracted from the Unified Medical Language System, we identified medical terms in the corpus with exact matching. The identified terms were annotated for negation, temporality, and experiencer properties. To adapt the ConText algorithm, we translated English trigger terms to Dutch and added several general and document specific enhancements, such as negation rules for general practitioners' entries and a regular expression based temporality module.

RESULTS:

The ContextD algorithm utilized 41 unique triggers to identify the contextual properties in the clinical corpus. For the negation property, the algorithm obtained an F-score from 87% to 93% for the different document types. For the experiencer property, the F-score was 99% to 100%. For the historical and hypothetical values of the temporality property, F-scores ranged from 26% to 54% and from 13% to 44%, respectively.

CONCLUSIONS:

The ContextD showed good performance in identifying negation and experiencer property values across all Dutch clinical document types. Accurate identification of the temporality property proved to be difficult and requires further work. The anonymized and annotated Dutch clinical corpus can serve as a useful resource for further algorithm development.

PMID:
25432799
PMCID:
PMC4264258
DOI:
10.1186/s12859-014-0373-3
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for BioMed Central Icon for PubMed Central
Loading ...
Support Center