Extracting Rx information from clinical narrative.
OBJECTIVE
The authors used the i2b2 Medication Extraction Challenge to evaluate their entity extraction methods, contribute to the generation of a publicly available collection of annotated clinical notes, and start developing methods for ontology-based reasoning using structured information generated from the unstructured clinical narrative.


METHODS
Extraction of salient features of medication orders from the text of de-identified hospital discharge summaries was addressed with a knowledge-based approach using simple rules and lookup lists. The entity recognition tool, MetaMap, was combined with dose, frequency, and duration modules specifically developed for the Challenge as well as a prototype module for reason identification.


METHODS
Evaluation metrics and corresponding results were provided by the Challenge organizers.


RESULTS
The results indicate that robust rule-based tools achieve satisfactory results in extraction of simple elements of medication orders, but more sophisticated methods are needed for identification of reasons for the orders and durations.


CONCLUSIONS
Owing to the time constraints and nature of the Challenge, some obvious follow-on analysis has not been completed yet.


CONCLUSIONS
The authors plan to integrate the new modules with MetaMap to enhance its accuracy. This integration effort will provide guidance in retargeting existing tools for better processing of clinical text.
Mork
James G
JG
US National Library of Medicine, National Institutes of Health, Bethesda, MD, USA.
Bodenreider
Olivier
O
Demner-Fushman
Dina
D
Dogan
Rezarta Islamaj
RI
Lang
François-Michel
FM
Lu
Zhiyong
Z
Névéol
Aurélie
A
Peters
Lee
L
Shooshan
Sonya E
SE
Aronson
Alan R
AR
D057286:Electronic Health Records; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods*; D009323:Natural Language Processing; D010351:Patient Discharge; D004364:Pharmaceutical Preparations; D012985:Software Design
D004364:Pharmaceutical Preparations
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