A Case Study of the Incremental Utility for Disease Identification of Natural Language Processing in Electronic Medical Records

Background Information exists as unstructured medical text in healthcare databases. Such information is not routinely considered in safety surveillance but typically relies solely on structured (coded) data. Natural language processing (NLP) may allow the capture of concepts from unstructured data and thus enhance safety surveillance capability.