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ConText: an algorithm for determining negation, experiencer, and temporal status from clinical reports.

Henk Harkema¹, John N Dowling, Tyler Thornblade

¹Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA 15260, USA. heh23@pitt.edu

Journal of Biomedical Informatics

|May 14, 2009

Summary

ConText algorithm accurately identifies negated, hypothetical, and historical clinical conditions using simple text clues. This approach shows promise for clinical report analysis but needs further development for other condition statuses.

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Area of Science:

Natural Language Processing
Clinical Informatics
Computational Linguistics

Background:

Clinical reports contain crucial patient information, including the status of medical conditions.
Accurate interpretation of condition status (negated, hypothetical, historical, etc.) is vital for clinical decision-making.
Existing methods for identifying condition status often rely on complex models or manual review.

Purpose of the Study:

To introduce and evaluate the ConText algorithm for determining the contextual status of clinical conditions in reports.
To assess the effectiveness of a surface-level, lexicon-based approach for various condition properties.
To investigate the portability of this approach across different clinical report types.

Main Methods:

Developed the ConText algorithm, which uses lexical clues within the clinical report context.
Inferred condition status (negated, hypothetical, historical, other patient) based on these clues.
Evaluated algorithm performance across diverse clinical report types.

Main Results:

ConText achieved reasonable to good performance for negated, historical, and hypothetical conditions.
Performance was consistent across report types where these conditions were present.
Conditions experienced by someone other than the patient were infrequently identified in the dataset.

Conclusions:

A simple, surface-based approach like ConText is effective for identifying several contextual properties of clinical conditions.
Further advancements are needed to accurately determine if a condition is historical versus recent, requiring more than surface clues.
The ConText algorithm demonstrates potential for improving automated analysis of clinical narratives.