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RESEARCH OBJECTIVE To develop scalable informatics infrastructure for normalization of both structured and unstructured electronic health record (EHR) data into a unified, concept-based model for high-throughput phenotype extraction. MATERIALS AND METHODS Software tools and applications were developed to extract information from EHRs. Representative and(More)
OBJECTIVE The objective of the Strategic Health IT Advanced Research Project area four (SHARPn) was to develop open-source tools that could be used for the normalization of electronic health record (EHR) data for secondary use--specifically, for high throughput phenotyping. We describe the role of Intermountain Healthcare's Clinical Element Models ([CEMs](More)
(Mar. 2015). " A survey on the application of recurrent neural networks to statistical language modeling ". In: Computer Speech & Language 30.1, pp. 61–98.
Machine learning, natural language processing and information retrieval theory and applications, including constructing timelines from unstructured text, characterizing quality in educational webpages, identifying science misconceptions in student essays, learning feature-based models for literature search, and building information extraction models for(More)
Traditional packet classification algorithms in Giga bit Intrusion Detection System (GIDS) always focus on static characteristic of the signature and ignore the traffic characteristic totally. In this paper we argue that efficiency of the classification algorithm is up to how current traffic visits the tree, the more well-proportioned the classification(More)
It is well recognized that controlled medical terminologies play a critical role in Health Information Systems and Clinical Patient Record systems, but the creation and management of customized lists of terms ("picklists") remains a potential obstacle. We have been developing a sophisticated authoring tool that is fully integrated with our terminology(More)
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