Insight: An ontology-based integrated database and analysis platform for epilepsy self-management research

Area of Science:

• Biomedical Informatics
• Epilepsy Research
• Data Science

Background:

• Epilepsy self-management research generates diverse datasets.
• Lack of integrated platforms hinders comprehensive analysis.
• Managing Epilepsy Well Network aims to improve epilepsy care through research.

Purpose of the Study:

• To introduce Insight, an integrated database and analysis platform for epilepsy self-management research.
• To provide a centralized resource for accessing and analyzing data from multiple epilepsy studies.
• To facilitate data-driven insights for advancing epilepsy self-management strategies.

Main Methods:

• Development of an informatics platform using Ruby on Rails and OWL API.
• Integration of Common Data Elements and an epilepsy domain ontology for data standardization.
• Implementation of visualization tools and an interactive query interface for data exploration and cohort identification.
• Inclusion of Role-based Access Control for secure data management.

Main Results:

• Insight integrates data from five completed epilepsy self-management studies, encompassing over 400 participants and 7552 data points.
• The platform supports data integration, querying, visualization, and cohort identification.
• An automated ontology management module ensures up-to-date data mappings.
• Secure user access is managed by the Managing Epilepsy Well Network steering committee.

Conclusions:

• Insight serves as a unique informatics platform for epilepsy self-management research.
• The platform's features facilitate aggregate data analysis to inform future research directions.
• Continuous addition of new studies enhances the dataset's size and coverage, supporting robust analysis.