Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Comparative Study
. 2008 Nov 6:2008:404-8.

Comparing ICD9-encoded diagnoses and NLP-processed discharge summaries for clinical trials pre-screening: a case study

Li Li  1 , Herbert S Chase, Chintan O Patel, Carol Friedman, Chunhua Weng
Affiliations
Comparative Study

Comparing ICD9-encoded diagnoses and NLP-processed discharge summaries for clinical trials pre-screening: a case study

Li Li et al. AMIA Annu Symp Proc. .

Abstract

The prevalence of electronic medical record (EMR) systems has made mass-screening for clinical trials viable through secondary uses of clinical data, which often exist in both structured and free text formats. The tradeoffs of using information in either data format for clinical trials screening are understudied. This paper compares the results of clinical trial eligibility queries over ICD9-encoded diagnoses and NLP-processed textual discharge summaries. The strengths and weaknesses of both data sources are summarized along the following dimensions: information completeness, expressiveness, code granularity, and accuracy of temporal information. We conclude that NLP-processed patient reports supplement important information for eligibility screening and should be used in combination with structured data.

PubMed Disclaimer

Figures

Figure 1
Figure 1
Patients Retrieved by Both Queries: Set A=E+C=1,356: retrieved by ICD9; Set B=C+D=1,605: retrieved by MedLEE; Set C= 352: retrieved by both ICD9 and MedLEE; Set D=1,253: retrieved only by MedLEE; Set E=1,004: retrieved only by ICD9.
Figure 2
Figure 2
Example MedLEE Output
See this image and copyright information in PMC

References

    1. Sinackevich N, Tassignon J-P. Speeding the Critical Path. Applied Clinical Trials. 2004
    1. Aronsky D, Haug PJ, Lagor C, Dean NC. 2005. Accuracy of Administrative Data for Identifying Patients With Pneumonia; pp. 319–328. - PubMed
    1. Embi PJ, Jain A, Clark J, Bizjack S, Hornung R, et al. Effect of a Clinical Trial Alert System on Physician Participation in Trial Recruitment. Arch Intern Med. 2005;165:2272–2277. - PMC - PubMed
    1. Fiszman M, Chapman W, Aronsky D, Evans R, Haug P. Automatic Detection of Acute Bacterial Pneumonia from Chest X-ray Reports. J Am Med Inform Assoc. 2000;7:593–604. - PMC - PubMed
    1. Gundlapalli AV, South BR, Phansalkar S, Kinney AY, Shen S, et al. Application of Natural Language Processing to VA Electronic Health Records to Identify Phenotypic Characteristics for Clinical and Research Purposes. Proc of 2008 AMIA Summit on Translational Bioinformatics. 2008:36–40. - PMC - PubMed

Publication types

LinkOut - more resources