A Semantically Rich Cognitive Search Assistant For Clinical Notes
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Date
2017-01-01
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Department
Computer Science and Electrical Engineering
Program
Computer Science
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This item may be protected under Title 17 of the U.S. Copyright Law. It is made available by UMBC for non-commercial research and education. For permission to publish or reproduce, please see http://aok.lib.umbc.edu/specoll/repro.php or contact Special Collections at speccoll(at)umbc.edu
Distribution Rights granted to UMBC by the author.
Distribution Rights granted to UMBC by the author.
Abstract
There are many use cases in the medical industry and in research that require clinical information extraction from the narrative notes in electronic medical records. Significant advances have been made in recent years from using clinical text processing systems which rely heavily on the natural language processing. However, for text that is entered by the clinician at the point of care, where time efficiency is paramount, a shorthand style of text is used which is not amenable to this approach. This research describes a novel approach that is robust to grammatically deficient text. It relies on techniques that are able to incorporate micro-contexts by taking into account scope, proximity, and location of multiple interdependent matched expressions. The validity of this approach was established by employing it to create a semantically rich cognitive search assistant that runs in near real-time over the corpus of clinical notes from the Veterans Administration. The cognitive search assistant was able to extract occurrences of pain events in the text with a positive precision of 84%, a positive recall of 94%, and an F-score of 89% at a rate of 0.31 seconds per note. The extracted results are saved in a semantic representation that permits a reasoning system to be incorporated to perform cognitive searches when used in conjunction with predefined medical ontologies. The result is a semantically rich cognitive search assistant capable of near real-time structured search over clinical text that can be used in interactive applications such as clinical decision support.