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Related Experiment Videos

Automated concept-level information extraction to reduce the need for custom software and rules development.

Leonard W D'Avolio¹, Thien M Nguyen, Sergey Goryachev

¹Massachusetts Veterans Epidemiology Research and Information Center Cooperative Studies Coordinating Center, VA Boston Healthcare System, Jamaica Plain, Massachusetts 02130, USA. leonard.davolio@va.gov

Journal of the American Medical Informatics Association : JAMIA

|June 24, 2011

Summary

A new, generalizable approach to clinical natural language processing (NLP) automates concept extraction. This method achieved high precision and F-measure scores, reducing the need for custom software development.

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Area of Science:

Medical Informatics
Computational Linguistics

Background:

Clinical natural language processing (NLP) systems have shown promise but are rarely implemented in practice.
Developing custom software and rules for information extraction is a significant barrier to clinical NLP adoption.

Purpose of the Study:

To address the gap between promising NLP performance and limited clinical application.
To present a generalizable, graphical user interface-driven approach for concept-level information retrieval.
To reduce the need for extensive custom software and rules development in clinical NLP.

Main Methods:

Employed a 'learn by example' strategy combining open-source NLP pipelines and machine learning classifiers.
Utilized iterative evaluation of configurations to identify top-performing models.
Used data and metrics from the Fourth i2b2/VA Shared Task Challenge for concept extraction.

Main Results:

Achieved top F-measure scores of 0.83 for medical problems, 0.82 for treatments, and 0.83 for tests.
Demonstrated high precision (near or above 0.90) across all tasks, with recall lagging.
Attained an average F-measure of 0.83 with minimal end-user configuration time (<5 minutes).

Conclusions:

Fully automated and generalizable approaches can yield acceptable performance for concept-level information extraction.
The developed implementation is available for download, facilitating broader adoption.
High precision suggests potential for specific clinical information extraction tasks.