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J Am Med Inform Assoc. 2011 Sep-Oct;18(5):563-7. doi: 10.1136/amiajnl-2011-000164. Epub 2011 Apr 22.

MITRE system for clinical assertion status classification.

Author information

  • 1The MITRE Corporation, Bedford, Massachusetts 01730-1420, USA. cclark@mitre.org

Abstract

OBJECTIVE:

To describe a system for determining the assertion status of medical problems mentioned in clinical reports, which was entered in the 2010 i2b2/VA community evaluation 'Challenges in natural language processing for clinical data' for the task of classifying assertions associated with problem concepts extracted from patient records.

MATERIALS AND METHODS:

A combination of machine learning (conditional random field and maximum entropy) and rule-based (pattern matching) techniques was used to detect negation, speculation, and hypothetical and conditional information, as well as information associated with persons other than the patient.

RESULTS:

The best submission obtained an overall micro-averaged F-score of 0.9343.

CONCLUSIONS:

Using semantic attributes of concepts and information about document structure as features for statistical classification of assertions is a good way to leverage rule-based and statistical techniques. In this task, the choice of features may be more important than the choice of classifier algorithm.

PMID:
21515542
[PubMed - indexed for MEDLINE]
PMCID:
PMC3168316
Free PMC Article
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