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A simple algorithm for identifying negated findings and diseases in discharge summaries
W W Chapman1, W Bridewell, P Hanbury
1Center for Biomedical Informatics, 8084 Forbes Tower, University of Pittsburgh, Pittsburgh, PA 15213, USA. chapman@cbmi.upmc.edu
Journal of Biomedical Informatics
|July 19, 2002
Summary
NegEx, a simple algorithm, effectively identifies pertinent negatives in medical reports. This tool enhances patient data by extracting absent findings from narrative text.
Area of Science:
- Natural Language Processing
- Clinical Informatics
- Medical Record Analysis
Background:
- Narrative medical records contain valuable information often missed by structured data.
- Pertinent negatives, crucial for patient management, are typically not indexed in databases.
Purpose of the Study:
- To develop and evaluate a simple algorithm for identifying the presence or absence of findings/diseases in narrative medical reports.
- To assess the effectiveness of the NegEx algorithm in extracting pertinent negatives from clinical text.
Main Methods:
- Developed NegEx, a regular expression algorithm using negation phrases, filtering, and scope limitation.
- Compared NegEx performance against a baseline algorithm on 1000 sentences from discharge summaries.
- Evaluated 1235 findings and diseases for specificity, positive predictive value, and sensitivity.
Main Results:
- NegEx achieved 94.5% specificity and 84.5% positive predictive value, outperforming the baseline.
- NegEx demonstrated a sensitivity of 77.8%, maintaining reasonable performance.
- The algorithm successfully identified a significant portion of pertinent negatives from discharge summaries.
Conclusions:
- NegEx offers a low-implementation-effort solution for extracting pertinent negatives from clinical narratives.
- This algorithm can significantly improve the utilization of unstructured data in electronic health records.
- Automated identification of absent findings enhances patient data management and disease trend prediction.
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