The Epilepsy Ontology is a comprehensive ontology developed for epilepsy clinical decision support, translational research, patient care, and health system management. The Epilepsy Ontology models seizure types, syndromes, etiology, electrophysiology, medication, neuropathology, including immunohistochemistry, microscopy, and anatomical description, together with terms describing preclinical model systems. The Epilepsy Ontology has been integrated with generative artificial intelligence (AI)/LLMs and machine learning models for natural language processing (NLP), drug efficacy analysis, and translational research. View the Epilepsy and Seizure Ontology on BioPortal
Clinical Decision Support
The Epilepsy Ontology provides a standardized vocabulary for consistent data capture and integration across clinical systems. It powers MEDCIS, a clinical decision support platform designed for multi-center epilepsy research and care, with a focus on SUDEP. MEDCIS uses tools like OPIC to ensure structured, ontology-driven data collection from both retrospective notes and prospective forms. In a study of over 900 patients across major epilepsy centers, MEDCIS enabled unified data analysis, real-time cohort identification, and integration with signal visualization. It answered 74% of predefined clinical research questions—demonstrating its value in breaking down data silos, supporting diagnostics, and accelerating research and treatment planning.
Translational Research
In translational research, the Epilepsy Ontology bridges basic science and clinical care by harmonizing data from molecular studies, animal models, and human trials. This supports biomarker discovery and accelerates translation to clinical interventions. An extension of this work is the Dravet Syndrome AI (DSAI) platform, which uses generative AI and the ontology to analyze literature and basic science data. DSAI helps identify parallels between model systems and human cases, opening new research paths and closing the gap between lab and clinic.
Patient Care
The Epilepsy Ontology (EpSO) improves patient care by standardizing epilepsy knowledge to support evidence-based, personalized treatment. It aligns with key classification systems, such as the International League Against Epilepsy (inclusive of 2025 revisions) and the Four-Dimensional Epilepsy Classification (4D-EC) framework. To operationalize this, we developed a web-based tool guided by EpSO, enabling dynamic application of the 4D-EC framework. The ontology ensures semantic consistency across evolving standards, supporting more accurate diagnoses and informed treatment decisions.
Healthcare Management
In healthcare systems, the Epilepsy Ontology standardizes and integrates diverse clinical data—like imaging, microscopy, and anatomical findings—across institutions. It enables consistent data representation from EHRs and registries. Ontology-based feature engineering using the Epilepsy Ontology has improved diagnostic accuracy by 54% and cut model runtime by 94%, highlighting its value for real-time decision support. This supports more efficient, data-driven epilepsy care and better resource use.
Golnari, P., Prantzalos, K., Hood, V., Meskis, M.A., Isom, L.L., Wilcox, K., Parent, J.M., Lal, D., Lhatoo, S.D., Goodkin, H.P., Wirrell, E.C., Knupp, K.G., Patel, M., Loeb, J.A., Sullivan, J.E., Harte-Hargrove, L., Fureman, B.E., Buchhalter, J., Sahoo, S.S., 2025. Ontology Accelerates Few-Shot Learning Capability of Large Language Model: A Study in Extraction of Drug Efficacy in a Rare Pediatric Epilepsy. International Journal of Medical Informatics, 2025
Golnari, P., Prantzalos, K., Upadhyaya D., Buchhalter, J., Sahoo S.S., Human in the Loop: Embedding Medical Expert Input in Large Language Models for Clinical Applications. MEDINFO 2025.
Prantzalos, K., Golnari, P., Upadhyaya D., Thyagaraj S., Fernandez Baca-Vaca, G., Luders H., Sahoo S.S., Standardized Epilepsy Data Collection and Analysis Leveraging the Four-Dimensional Epilepsy Classification (4D-EC) Framework. MEDINFO 2025.
Sahoo, S.S., Kobow, K., Zhang, J., Buchhalter, J., Dayyani, M., Upadhyaya, D.P., Prantzalos, K., Bhattacharjee, M., Blumcke, I., Wiebe, S. and Lhatoo, S.D., 2022. Ontology-based feature engineering in machine learning workflows for heterogeneous epilepsy patient records. Scientific reports, 12(1), p.19430.
Prantzalos, K., Zhang, J., Shafiabadi, N., Fernandez-BacaVaca, G. and Sahoo, S.S., 2022, February. Epilepsy-Connect: An Integrated Knowledgebase for Characterizing Alterations in Consciousness State of Pharmacoresistant Epilepsy Patients. In AMIA Annual Symposium Proceedings (Vol. 2021, p. 1019).
Sahoo, S.S., Zhang, G.Q., Bamps, Y., Fraser, R., Stoll, S., Lhatoo, S.D., Tatsuoka, C., Sams, J., Welter, E. and Sajatovic, M., 2016. Managing information well: Toward an ontology-driven informatics platform for data sharing and secondary use in epilepsy self-management research centers. Health informatics journal, 22(3), pp.548-561.
Cui, L., Sahoo, S.S., Lhatoo, S.D., Garg, G., Rai, P., Bozorgi, A. and Zhang, G.Q., 2014. Complex epilepsy phenotype extraction from narrative clinical discharge summaries. Journal of biomedical informatics, 51, pp.272-279.
Zhang, G.Q., Cui, L., Lhatoo, S., Schuele, S.U. and Sahoo, S.S., 2014. MEDCIS: multi-modality epilepsy data capture and integration system. In AMIA Annual Symposium Proceedings (Vol. 2014, p. 1248). American Medical Informatics Association.
Sahoo, S.S., Zhang, G.Q. and Lhatoo, S.D., 2013. Epilepsy informatics and an ontology‐driven infrastructure for large database research and patient care in epilepsy. Epilepsia, 54(8), pp.1335-1341.
This work has been funded in part by the Dravet Syndrome Foundation (DSF), International League Against Epilepsy (ILAE), and the US National Institutes of Health (NIH). Some of the funding agencies retain intellectual ownership of the ontology, together with the Case Western Reserve University and other collaborating institutions.