High accuracy information extraction of medication information from clinical notes: 2009 i2b2 medication extraction challenge.
OBJECTIVE
Medication information comprises a most valuable source of data in clinical records. This paper describes use of a cascade of machine learners that automatically extract medication information from clinical records.


METHODS
Authors developed a novel supervised learning model that incorporates two machine learning algorithms and several rule-based engines.


METHODS
Evaluation of each step included precision, recall and F-measure metrics. The final outputs of the system were scored using the i2b2 workshop evaluation metrics, including strict and relaxed matching with a gold standard.


RESULTS
Evaluation results showed greater than 90% accuracy on five out of seven entities in the name entity recognition task, and an F-measure greater than 95% on the relationship classification task. The strict micro averaged F-measure for the system output achieved best submitted performance of the competition, at 85.65%.


CONCLUSIONS
Clinical staff will only use practical processing systems if they have confidence in their reliability. Authors estimate that an acceptable accuracy for a such a working system should be approximately 95%. This leaves a significant performance gap of 5 to 10% from the current processing capabilities.


CONCLUSIONS
A multistage method with mixed computational strategies using a combination of rule-based classifiers and statistical classifiers seems to provide a near-optimal strategy for automated extraction of medication information from clinical records.
Patrick
Jon
J
Faculty of Engineering and IT, the University of Sydney, Sydney, Australia. jonpat@it.usyd.edu.au
Li
Min
M
D001185:Artificial Intelligence; D057286:Electronic Health Records; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods*; D009323:Natural Language Processing; D004364:Pharmaceutical Preparations
D004364:Pharmaceutical Preparations
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