Distinguishing historical from current problems in clinical reports: which textual features help?

Authors:
Danielle L. Mowery;Henk Harkema;John N. Dowling;Jonathan L. Lustgarten;Wendy W. Chapman
Affiliations:
University of Pittsburgh, Pittsburgh, PA;University of Pittsburgh, Pittsburgh, PA;University of Pittsburgh, Pittsburgh, PA;University of Pittsburgh, Pittsburgh, PA;University of Pittsburgh, Pittsburgh, PA
Venue:
BioNLP '09 Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing
Year:
2009

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Abstract

Determining whether a condition is historical or recent is important for accurate results in biomedicine. In this paper, we investigate four types of information found in clinical text that might be used to make this distinction. We conducted a descriptive, exploratory study using annotation on clinical reports to determine whether this temporal information is useful for classifying conditions as historical or recent. Our initial results suggest that few of these feature values can be used to predict temporal classification.