2025/01/17 by Rion Brattig Correia, Jordan C. Rozum, Leonard E. Cross +12 · 1 voice · 1 citation
Computer Science · Health Professions · #Advanced Graph Neural Networks #Electronic Health Records Systems #Machine Learning in Healthcare
paper · doi:10.1093/jamia/ocaf012
openalex publication_date 2025/01/17 · openalex created_date 2025/02/01 · openalex updated_date 2026/07/22
OBJECTIVES: Report the development of the patient-centered myAURA application and suite of methods designed to aid epilepsy patients, caregivers, and clinicians in making decisions about self-management and care. MATERIALS AND METHODS: myAURA rests on an unprecedented collection of epilepsy-relevant heterogeneous data resources, such as biomedical databases, social media, and electronic health records (EHRs). We use a patient-centered biomedical dictionary to link the collected data in a multilayer knowledge graph (KG) computed with a generalizable, open-source methodology. RESULTS: Our approach is based on a novel network sparsification method that uses the metric backbone of weighted graphs to discover important edges for inference, recommendation, and visualization. We demonstrate by studying drug-drug interaction from EHRs, extracting epilepsy-focused digital cohorts from social media, and generating a multilayer KG visualization. We also present our patient-centered design and pilot-testing of myAURA, including its user interface. DISCUSSION: The ability to search and explore myAURA's heterogeneous data sources in a single, sparsified, multilayer KG is highly useful for a range of epilepsy studies and stakeholder support. CONCLUSION: Our stakeholder-driven, scalable approach to integrating traditional and nontraditional data sources enables both clinical discovery and data-powered patient self-management in epilepsy and can be generalized to other chronic conditions.