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Proceedings of the AMIA Symposium logoLink to Proceedings of the AMIA Symposium
. 1999:256–260.

Automating a severity score guideline for community-acquired pneumonia employing medical language processing of discharge summaries.

C Friedman 1, C Knirsch 1, L Shagina 1, G Hripcsak 1
PMCID: PMC2232753  PMID: 10566360

Abstract

Obtaining encoded variables is often a key obstacle to automating clinical guidelines. Frequently the pertinent information occurs as text in patient reports, but text is inadequate for the task. This paper describes a retrospective study that automates determination of severity classes for patients with community-acquired pneumonia (i.e. classifies patients into risk classes 1-5), a common and costly clinical problem. Most of the variables for the automated application were obtained by writing queries based on output generated by MedLEE1, a natural language processor that encodes clinical information in text. Comorbidities, vital signs, and symptoms from discharge summaries as well as information from chest x-ray reports were used. The results were very good because when compared with a reference standard obtained manually by an independent expert, the automated application demonstrated an accuracy, sensitivity, and specificity of 93%, 92%, and 93% respectively for processing discharge summaries, and 96%, 87%, and 98% respectively for chest x-rays. The accuracy for vital sign values was 85%, and the accuracy for determining the exact risk class was 80%. The remaining 20% that did not match exactly differed by only one class.

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Selected References

These references are in PubMed. This may not be the complete list of references from this article.

  1. Aronsky D., Haug P. J. Diagnosing community-acquired pneumonia with a Bayesian network. Proc AMIA Symp. 1998:632–636. [PMC free article] [PubMed] [Google Scholar]
  2. Baud R. H., Rassinoux A. M., Scherrer J. R. Natural language processing and semantical representation of medical texts. Methods Inf Med. 1992 Jun;31(2):117–125. [PubMed] [Google Scholar]
  3. Fine M. J., Auble T. E., Yealy D. M., Hanusa B. H., Weissfeld L. A., Singer D. E., Coley C. M., Marrie T. J., Kapoor W. N. A prediction rule to identify low-risk patients with community-acquired pneumonia. N Engl J Med. 1997 Jan 23;336(4):243–250. doi: 10.1056/NEJM199701233360402. [DOI] [PubMed] [Google Scholar]
  4. Friedman C., Alderson P. O., Austin J. H., Cimino J. J., Johnson S. B. A general natural-language text processor for clinical radiology. J Am Med Inform Assoc. 1994 Mar-Apr;1(2):161–174. doi: 10.1136/jamia.1994.95236146. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Friedman C. Towards a comprehensive medical language processing system: methods and issues. Proc AMIA Annu Fall Symp. 1997:595–599. [PMC free article] [PubMed] [Google Scholar]
  6. Gundersen M. L., Haug P. J., Pryor T. A., van Bree R., Koehler S., Bauer K., Clemons B. Development and evaluation of a computerized admission diagnoses encoding system. Comput Biomed Res. 1996 Oct;29(5):351–372. doi: 10.1006/cbmr.1996.0026. [DOI] [PubMed] [Google Scholar]
  7. Haug P. J., Ranum D. L., Frederick P. R. Computerized extraction of coded findings from free-text radiologic reports. Work in progress. Radiology. 1990 Feb;174(2):543–548. doi: 10.1148/radiology.174.2.2404321. [DOI] [PubMed] [Google Scholar]
  8. Hripcsak G., Friedman C., Alderson P. O., DuMouchel W., Johnson S. B., Clayton P. D. Unlocking clinical data from narrative reports: a study of natural language processing. Ann Intern Med. 1995 May 1;122(9):681–688. doi: 10.7326/0003-4819-122-9-199505010-00007. [DOI] [PubMed] [Google Scholar]
  9. Hripcsak G., Kuperman G. J., Friedman C., Heitjan D. F. A reliability study for evaluating information extraction from radiology reports. J Am Med Inform Assoc. 1999 Mar-Apr;6(2):143–150. doi: 10.1136/jamia.1999.0060143. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Jain N. L., Friedman C. Identification of findings suspicious for breast cancer based on natural language processing of mammogram reports. Proc AMIA Annu Fall Symp. 1997:829–833. [PMC free article] [PubMed] [Google Scholar]
  11. Knirsch C. A., Jain N. L., Pablos-Mendez A., Friedman C., Hripcsak G. Respiratory isolation of tuberculosis patients using clinical guidelines and an automated clinical decision support system. Infect Control Hosp Epidemiol. 1998 Feb;19(2):94–100. doi: 10.1086/647773. [DOI] [PubMed] [Google Scholar]
  12. Sager N., Lyman M., Bucknall C., Nhan N., Tick L. J. Natural language processing and the representation of clinical data. J Am Med Inform Assoc. 1994 Mar-Apr;1(2):142–160. doi: 10.1136/jamia.1994.95236145. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Zingmond D., Lenert L. A. Monitoring free-text data using medical language processing. Comput Biomed Res. 1993 Oct;26(5):467–481. doi: 10.1006/cbmr.1993.1033. [DOI] [PubMed] [Google Scholar]

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