Making EHR data extraction more exact is possible

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The medical information contained within EHRs is undoubtedly valuable to many of healthcare’s stakeholders, but its presentation as unstructured text often makes locating necessary information difficult, according to a study published July 6 in the Journal of the American Medical Informatics Association.


Using a novel method to detect named entities within EHRs, however, researchers demonstrated that constructing more effective information extraction systems is possible at low costs.


Most named entity recognition methods for collecting information from datasets use conditional random field (CRF) recognizers, which rely heavily on local contexts surrounding named entities and assume that similar local contexts lead to the same judgements, according to the study’s author, Eric I-Chao Chang, MD, of Microsoft Research Asia.


The problem with data extraction methods relying solely on CRF recognizers is that they often deliver contradictory information.

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