Creating a text classifier to detect radiology reports describing mediastinal findings associated with inhalational anthrax and other disorders

OBJECTIVE

The aim of this study was to create a classifier for automatic detection of chest radiograph reports consistent with the mediastinal findings of inhalational anthrax.

DESIGN

The authors used the Identify Patient Sets (IPS) system to create a key word classifier for detecting reports describing mediastinal findings consistent with anthrax and compared their performances on a test set of 79,032 chest radiograph reports.

MEASUREMENTS

Area under the ROC curve was the main outcome measure of the IPS classifier. Sensitivity and specificity of an initial IPS model were calculated based on an existing key word search and were compared against a Boolean version of the IPS classifier.

RESULTS

The IPS classifier received an area under the ROC curve of 0.677 (90% CI = 0.628 to 0.772) with a specificity of 0.99 and maximum sensitivity of 0.35. The initial IPS model attained a specificity of 1.0 and a sensitivity of 0.04.

CONCLUSION

The IPS system is a useful tool for helping domain experts create a statistical key word classifier for textual reports that is a potentially useful component in surveillance of radiographic findings suspicious for anthrax.

Detecting adverse drug events in discharge summaries using variations on the simple Bayes model

Detection and prevention of adverse events and, in particular, adverse drug events (ADEs), is an important problem in health care today. We describe the implementation and evaluation of four variations on the simple Bayes model for identifying ADE-related discharge summaries. Our results show that these probabilistic techniques achieve an ROC curve area of up to 0.77 in correctly determining which patient cases should be assigned an ADE-related ICD-9-CM code. These results suggest a potential for these techniques to contribute to the development of an automated system that helps identify ADEs, as a step toward further understanding and preventing them.

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.

The effect of substituents on geminal proton–proton coupling constants

A series of 2-benzyloxytetrahydropyrans bearing substituents at the ortho, meta, and para positions of the benzene ring have been synthesized. From the nuclear magnetic resonance spectrum of each compound, the geminal coupling constant (J) between the non-equivalent protons of the benzylic methylene group was determined. The geminal coupling constant for the methylene group of several benzyl sulfoxides was also measured. In the ether series it was found that J varied from 11.1 to 13.3 c.p.s. and was directly proportional to the Hammett σ value for the meta and para substituents. In the sulfoxides, however, J was unaffected by the substituent. On the basis of the molecular orbital theory of geminal coupling constants and steric considerations, it is proposed that the sensitivity of J to the substituent effect is dependent upon the conformation of the molecules with respect to the Ar—CH2 bond. The potential utility of this relation as a method of conformational analysis is discussed.

Non-equivalence of vicinal proton–fluorine coupling constants in a CF3—CH2 group

The proton magnetic resonance spectrum of the tetrahydropyranyl ether of 2,2,2-trifluoroethanol has been measured in several solvents and at different temperatures. Analysis of the ABX3 pattern produced by the trifluoroethyl group revealed a unique feature. Under all conditions, it was found that the vicinal coupling constants JAX and JBX had different magnitudes. This non-equivalence is interpreted as evidence that the C—HA and C—HB bonds of the trifluoroethyl group differ electronically. A possible relation between this finding and the anomeric effect is discussed. In contrast to the above results, the tetrahydropyranyl ether of ethanol showed equal vicinal proton–proton coupling constants in the ethyl group.