Division of Medical Informatics, School of Medicine, Case Western Reserve University, Cleveland, Ohio 44106, USA.
Epilepsia. 2013 Aug;54(8):1335-41. doi: 10.1111/epi.12211. Epub 2013 May 3.
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.
癫痫学界越来越认识到，需要有一种现代的分类系统，这种系统还能方便地与有效的信息学工具相整合。美国科学和技术总统顾问委员会（PCAST）在 2010 年的报告中指出，信息学是改善患者护理质量、推动临床研究和降低医疗服务成本的关键资源。以正式的知识模型或本体为基础的有效的癫痫信息学基础设施，可以利用日益增多的多模态数据来改进（1）临床决策支持，（2）患者及其家属获取信息，（3）更方便的数据共享，以及（4）加快对临床数据的二次利用。以癫痫领域本体的形式对国际抗癫痫联盟（ILAE）分类系统的建议进行建模，对于在各种应用中（包括电子健康记录系统和临床应用）一致使用术语是必不可少的。在这篇综述中，我们讨论了癫痫中的数据管理问题，并探讨了本体驱动的信息学基础设施的益处及其在癫痫研究中采用“数据驱动”范例中的作用。
