Epilepsy informatics and an ontology-driven infrastructure for large database research and patient care in epilepsy

Authors

  • Satya S. Sahoo,

    1. Division of Medical Informatics, School of Medicine, Case Western Reserve University, Cleveland, Ohio, U.S.A
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  • Guo-Qiang Zhang,

    1. Division of Medical Informatics, School of Medicine, Case Western Reserve University, Cleveland, Ohio, U.S.A
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  • Samden D. Lhatoo

    Corresponding author
    1. Department of Neurology, School of Medicine, Case Western Reserve University, Cleveland, Ohio, U.S.A
    • Address correspondence to Samden D. Lhatoo, Neurological Institute, Case Medical Center and Case Western Reserve University School of Medicine, Epilepsy Center – Lakeside 3222A, 11100 Euclid Ave, Cleveland, OH 44106, U.S.A. E-mail: samden.lhatoo@uhhospitals.org

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Summary

The epilepsy community increasingly recognizes the need for a modern classification system that can also be easily integrated with effective informatics tools. The 2010 reports by the United States President's Council of Advisors on Science and Technology (PCAST) identified informatics as a critical resource to improve quality of patient care, drive clinical research, and reduce the cost of health services. An effective informatics infrastructure for epilepsy, which is underpinned by a formal knowledge model or ontology, can leverage an ever increasing amount of multimodal data to improve (1) clinical decision support, (2) access to information for patients and their families, (3) easier data sharing, and (4) accelerate secondary use of clinical data. Modeling the recommendations of the International League Against Epilepsy (ILAE) classification system in the form of an epilepsy domain ontology is essential for consistent use of terminology in a variety of applications, including electronic health records systems and clinical applications. In this review, we discuss the data management issues in epilepsy and explore the benefits of an ontology-driven informatics infrastructure and its role in adoption of a “data-driven” paradigm in epilepsy research.

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