A Simple Algorithm for Identifying Negated Findings and Diseases in Discharge Summaries

Abstract

Narrative reports in medical records contain a wealth of information that may augment structured data for managing patient information and predicting trends in diseases. Pertinent negatives are evident in text but are not usually indexed in structured databases. The objective of the study reported here was to test a simple algorithm for determining whether a finding or disease mentioned within narrative medical reports is present or absent. We developed a simple regular expression algorithm called NegEx that implements several phrases indicating negation, filters out sentences containing phrases that falsely appear to be negation phrases, and limits the scope of the negation phrases. We compared NegEx against a baseline algorithm that has a limited set of negation phrases and a simpler notion of scope. In a test of 1235 findings and diseases in 1000 sentences taken from discharge summaries indexed by physicians, NegEx had a specificity of 94.5% (versus 85.3% for the baseline), a positive predictive value of 84.5% (versus 68.4% for the baseline) while maintaining a reasonable sensitivity of 77.8% (versus 88.3% for the baseline). We conclude that with little implementation effort a simple regular expression algorithm for determining whether a finding or disease is absent can identify a large portion of the pertinent negatives from discharge summaries.

Evaluation of an Automated Information Extraction Tool for Imaging Data Elements to Populate a Breast Cancer Screening Registry

“…iSCOUT was able to perform information extraction using rule-based classification for several findings. The algorithm included (1) finding query terms (and corresponding lexical and semantic variants) within radiology reports; (2) excluding reports that contained findings that were negated using a rule-based algorithm similar to one described previously [35]; (3) extracting the laterality of each imaging finding within radiology reports that contained them by finding the nearest word referring to sidedness (i.e., "left", "right," or "bilateral") in the same sentence as the imaging finding. If a word(s) corresponding to sidedness (i.e., "left", "right," or "bilateral") was not stated in the same sentence, the sidedness that was closest to the finding in preceding sentences was noted, where distance was defined as the number of words separating the finding from the sidedness.…”

Evaluating the Referring Physician’s Clinical History and Indication as a Means for Communicating Chronic Conditions That Are Pertinent at the Point of Radiologic Interpretation

“…All segmented sentences were individually parsed by MetaMap [9] for Systemized Nomenclature of Medicine-Clinical Terms (SNOMED CT) concepts. Using its innate negation detection module (NegEx [8]), MetaMap checks if a concept appears negated or not. The de-identified reports and derived sentence and concept tables were stored in a MySQL (version 5.2.38, Oracle) database.…”

Health-Terrain: Visualizing Large Scale Health Data

“…This was done with the help of UMLS 3 database which is a repository of clinical and health related terms. Once the entities were extracted using NER, negation analysis was applied using NEGEX algorithm 4 to remove negated terms. Figure 1 shows the process that was used in extracting this vital information from the reports.…”