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Improving the sensitivity of the problem list in an intensive care unit by using natural language processing.

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Department of Medical Informatics, University of Utah, Salt Lake City, Utah, U.S.


To improve the completeness of an electronic problem list, we have developed a system using Natural Language Processing to automatically extract potential medical problems from clinical free-text documents; these problems are then proposed for inclusion in an electronic problem list management application. A prospective randomized controlled evaluation of this system in an intensive care unit is reported here. A total of 105 patients were randomly assigned to a control or an intervention group. In the latter, patients had their documents analyzed by the system and medical problems discovered were proposed for inclusion into their problem list. In this population, our system significantly increased the sensitivity of the problem lists, from 8.9% to 41%, and to 77.4% if problems automatically proposed but not acknowledged by users were also considered.

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