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.

Design

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

Measurements

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%.

Limitations

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.

Conclusion

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.Many of the potential benefits of the electronic medical record (EMR) rely significantly on our ability to automatically process the free-text content in the EMR. To understand the limitations and difficulties of exploiting the EMR we have designed an information extraction engine to identify medication events within patient discharge summaries, as specified by the i2b2 medication extraction shared task.