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Automatic extraction of medication information from medical discharge summaries

Yang, Hui (2010). Automatic extraction of medication information from medical discharge summaries. Journal of the American Medical Informatics Association, 17(5) pp. 545–548.

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Objective This article describes a system developed for the 2009 i2b2 Medication Extraction Challenge. The purpose of this challenge is to extract medication information from hospital discharge summaries.

Design The system explored several linguistic natural language processing techniques (eg, term-based and token-based rule matching) to identify medication-related information in the narrative text. A number of lexical resources was constructed to profile lexical or morphological features for different categories of medication constituents.

Measurements Performance was evaluated in terms of the micro-averaged F-measure at the horizontal system level.

Results The automated system performed well, and achieved an F-micro of 80% for the term-level results and 81% for the token-level results, placing it sixth in exact matches and fourth in inexact matches in the i2b2 competition.

Conclusion The overall results show that this relatively simple rule-based approach is capable of tackling multiple entity identification tasks such as medication extraction under situations in which few training documents are annotated for machine learning approaches, and the entity information can be characterized with a set of feature tokens.

Item Type: Journal Item
Copyright Holders: 2010 American Medical Informatics Association
ISSN: 1527-974X
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Computing and Communications
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Research Group: Centre for Research in Computing (CRC)
Item ID: 23768
Depositing User: Hui Yang
Date Deposited: 19 Oct 2010 09:25
Last Modified: 02 May 2018 13:17
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