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myAURA: a personalized health library for epilepsy management via knowledge graph sparsification and visualization

2026-01-01Artigo científico Acesso aberto
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dc.contributor.authorCorreia, Rion Brattig
dc.contributor.authorRozum, Jordan C.
dc.contributor.authorCross, Leonard
dc.contributor.authorFelag, Jack
dc.contributor.authorGallant, Michael
dc.contributor.authorGuo, Ziqi
dc.contributor.authorHerr II, Bruce W.
dc.contributor.authorMin, Aehong
dc.contributor.authorSanchez-Valle, Jon
dc.contributor.authorRocha, Deborah Stungis
dc.contributor.authorValencia, Alfonso
dc.contributor.authorWang, Xuan
dc.contributor.authorBörner, Katy
dc.contributor.authorMiller, Wendy
dc.contributor.authorRocha, Luis M.
dc.date.accessioned2025-02-25T17:07:07Z
dc.date.available2025-02-25T17:07:07Z
dc.date.issued2026-01-01
dc.description.abstractObjectives: 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.pt_PT
dc.description.versioninfo:eu-repo/semantics/acceptedVersionpt_PT
dc.identifier.doi10.1093/jamia/ocaf012pt_PT
dc.identifier.eid105026383160
dc.identifier.issn1067-5027
dc.identifier.pmid39890454
dc.identifier.urihttp://hdl.handle.net/10400.14/48293
dc.identifier.wos001410117600001
dc.language.isoengpt_PT
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/pt_PT
dc.subjectData visualizationpt_PT
dc.subjectEpilepsypt_PT
dc.subjectSelf-managementpt_PT
dc.subjectSemantic webpt_PT
dc.subjectSocial mediapt_PT
dc.subjectSystems analysispt_PT
dc.titlemyAURA: a personalized health library for epilepsy management via knowledge graph sparsification and visualizationpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage181
oaire.citation.issue1
oaire.citation.startPage167
oaire.citation.titleJournal of the American Medical Informatics Association : JAMIApt_PT
oaire.citation.volume33
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT

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