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Review
. 2013 Aug;54(8):1335-41.
doi: 10.1111/epi.12211. Epub 2013 May 3.

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

Satya S Sahoo  1 , Guo-Qiang Zhang, Samden D Lhatoo
Affiliations
Review

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

Satya S Sahoo et al. Epilepsia. 2013 Aug.

Abstract

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.

Keywords: Biomedical ontologies; Clinical research; Epilepsy; Informatics; Large scale data management.

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Conflict of interest statement

Disclosure: None of the authors has any conflict of interest to disclose. We confirm that we have read the Journal's position on issues involved in ethical publication and affirm that this report is consistent with those guidelines.

Figures

Figure 1
Figure 1
A snapshot of epilepsy ontology classes representing etiology, brain anatomy, and EEG-related terms (reusing NEMO classes).
Figure 2
Figure 2
Initial work in dynamically creating a class hierarchy of Typical Absence Seizure subtypes using FCA. (A) Shows the two-dimensional matrix of attributes and epilepsy types used as input to FCA, (B) illustrates the concept hierarchy (lattice) with higher-level relationships that are often not apparent from the lower level classification, such as the fact that Eyelid Myoclonia with Absences is a subtype of Typical Absence Seizure, and (C) shows the construction of an ontology class hierarchy from the concept hierarchy.
See this image and copyright information in PMC

Comment in

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