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J Am Med Inform Assoc 17:528-531 doi:10.1136/jamia.2010.003855
  • Research paper

Integrating existing natural language processing tools for medication extraction from discharge summaries

  1. Hua Xu1
  1. 1Department of Biomedical Informatics, Vanderbilt University, School of Medicine, Nashville, Tennessee, USA
  2. 2Department of Medicine, Vanderbilt University, School of Medicine, Nashville, Tennessee, USA
  3. 3Department of Medicine, University of Tennessee at Memphis, Memphis, Tennessee, USA
  1. Correspondence to Hua Xu, Department of Biomedical Informatics, Vanderbilt University, School of Medicine, 2209 Garland Avenue EBL 412, Nashville, TN 37232, USA; hua.xu{at}vanderbilt.edu
  • Received 19 February 2010
  • Accepted 25 June 2010

Abstract

Objective To develop an automated system to extract medications and related information from discharge summaries as part of the 2009 i2b2 natural language processing (NLP) challenge. This task required accurate recognition of medication name, dosage, mode, frequency, duration, and reason for drug administration.

Design We developed an integrated system using several existing NLP components developed at Vanderbilt University Medical Center, which included MedEx (to extract medication information), SecTag (a section identification system for clinical notes), a sentence splitter, and a spell checker for drug names. Our goal was to achieve good performance with minimal to no specific training for this document corpus; thus, evaluating the portability of those NLP tools beyond their home institution. The integrated system was developed using 17 notes that were annotated by the organizers and evaluated using 251 notes that were annotated by participating teams.

Measurements The i2b2 challenge used standard measures, including precision, recall, and F-measure, to evaluate the performance of participating systems. There were two ways to determine whether an extracted textual finding is correct or not: exact matching or inexact matching. The overall performance for all six types of medication-related findings across 251 annotated notes was considered as the primary metric in the challenge.

Results Our system achieved an overall F-measure of 0.821 for exact matching (0.839 precision; 0.803 recall) and 0.822 for inexact matching (0.866 precision; 0.782 recall). The system ranked second out of 20 participating teams on overall performance at extracting medications and related information.

Conclusions The results show that the existing MedEx system, together with other NLP components, can extract medication information in clinical text from institutions other than the site of algorithm development with reasonable performance.

Footnotes

  • Funding This study was partially supported by grants from the US NIH: NHGRI U01 HG004603, NLM R01-LM007995-05, and NCI R01CA141307-01. The project described was supported in part by the i2b2 initiative, Award Number U54LM008748 from the National Library of Medicine. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Library of Medicine or the National Institutes of Health.

  • Competing interests None.

  • Provenance and peer review Not commissioned; externally peer reviewed.

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