Mining Electronic Health Records to Guide and Support Clinical Decision Support Systems

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Author(s): Jitendra Jonnagaddala (University of New South Wales, Australia), Hong-Jie Dai (National Taitung University, Taiwan), Pradeep Ray (University of New South Wales, Australia)and Siaw-Teng Liaw (University of New South Wales, Australia)
Copyright: 2016
Pages: 18
Source title: Improving Health Management through Clinical Decision Support Systems
Source Author(s)/Editor(s): Jane D. Moon (The University of Melbourne, Australia)and Mary P. Galea (The University of Melbourne, Australia)
DOI: 10.4018/978-1-4666-9432-3.ch012

Keywords: Business & Management / Decision Support Systems / Health Information Systems / Medical Information Science Reference

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Abstract

Clinical decision support systems require well-designed electronic health record (EHR) systems and vice versa. The data stored or captured in EHRs are diverse and include demographics, billing, medications, and laboratory reports; and can be categorized as structured, semi-structured and unstructured data. Various data and text mining techniques have been used to extract these data from EHRs for use in decision support, quality improvement and research. Mining EHRs has been used to identify cohorts, correlated phenotypes in genome-wide association studies, disease correlations and risk factors, drug-drug interactions, and to improve health services. However, mining EHR data is a challenge with many issues and barriers. The aim of this chapter is to discuss how data and text mining techniques may guide and support the building of improved clinical decision support systems.

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Mining Electronic Health Records to Guide and Support Clinical Decision Support Systems

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