Document Type
Poster
Publication Date
Spring 4-17-2026
Abstract
Epilepsy affects over five million people globally each year, yet consistent clinical diagnosis remains a persistent challenge due to the lack of standardized classification workflows across medical institutions. The Four-Dimensional Epilepsy Classification (4D-EC) framework, developed by Lüders et al., provides a comprehensive structure for characterizing paroxysmal events across four dimensions: seizure semiology, epileptogenic zone, etiology, and comorbidities. Despite its clinical and educational value, no dedicated informatics platform existed to support its routine use until recently, limiting widespread adoption among clinicians and trainees. This project addresses that gap by implementing a full-stack web application that operationalizes the 4D-EC framework for clinical and educational use. The platform was developed using React (TypeScript) on the frontend and Django (Python) with a PostgreSQL database on the backend, following a three-tier architecture with RESTful API communication and token-based authentication. The application guides users through a structured, multi-step classification workflow covering all four dimensions, with adaptive page visibility based on event type, a hierarchical clinical findings database with real-time search, drag-and-drop semiological ordering, and automated summary generation. Completed features include secure multi-user authentication, full data persistence with state reconstruction, and a responsive interface built with Radix UI components. Ongoing development targets AI-powered chatbot integration for contextual guidance and terminology clarification, PDF export, edit and delete functionality for saved classifications, and comprehensive unit and integration testing. The platform is designed to reduce diagnostic inconsistency, lower the barrier to 4D-EC adoption, and support training of epileptologists by embedding the framework directly into a practical, accessible clinical tool.
Language
English
Publication Title
Intersections Poster Symposium
Rights
© The Author(s). This is an Open Access work distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Panda, Attiksh A.; Desai, Deep; Zabarov, Artem; Prantzalos, Katrina D.; Sahoo, Satya S.; and Xu, Shuai, "A Backend Database Architecture for Persistent Epilepsy Classification Records" (2026). Student Scholarship. 683.
https://commons.case.edu/studentworks/683
PowerPoint file version
Included in
Databases and Information Systems Commons, Health Information Technology Commons, Software Engineering Commons