A simple algorithm for identifying negated findings and diseases in discharge summaries.

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 negation phrases in narrative clinical reports.

OBJECTIVE: Automatically identifying findings or diseases described in clinical textual reports requires determining whether clinical observations are present or absent. We evaluate the use of negation phrases and the frequency of negation in free-text clinical reports. METHODS: A simple negation algorithm was applied to ten types of clinical reports (n=42,160) dictated during July 2000. We counted how often each of 66 negation phrases was used to mark a clinical observation as absent. Physicians read a random sample of 400 sentences, and precision was calculated for the negation phrases. We measured what proportion of clinical observations were marked as absent. RESULTS: The negation algorithm was triggered by sixty negation phrases with just seven of the phrases accounting for 90% of the negations. The negation phrases received an overall precision of 97%, with "not" earning the lowest precision of 63%. Between 39% and 83% of all clinical observations were identified as absent by the negation algorithm, depending on the type of report analyzed. The most frequently used clinical observations were negated the majority of the time. CONCLUSION: Because clinical observations in textual patient records are frequently negated, identifying accurate negation phrases is important to any system processing these reports.

Irrational beliefs, optimism, pessimism, and psychological distress: a preliminary examination of differential effects in a college population.

The present study compared the effects of irrational beliefs measured by the Survey of Personal Beliefs (SPB) and optimism and pessimism as measured by the revised Life Orientation Test (LOT-R) on depressive and anxious symptoms 6 weeks later. Results of analysis of variances for both measures of psychological distress indicated a significant main effect for pessimism only. Implications for Ellis Rational Emotive Therapy are discussed.