Objective: We report the development of the patient-centered myAURA application and suite of methods designed to aid epilepsy patients, caregivers, and researchers in making decisions about care and self-management. Materials and Methods: myAURA rests on the federation of an unprecedented collection of heterogeneous data resources relevant to epilepsy, such as biomedical databases, social media, and electronic health records. A generalizable, open-source methodology was developed to compute a multi-layer knowledge graph linking all this heterogeneous data via the terms of a human-centered biomedical dictionary. Results: The power of the approach is first exemplified in the study of the drug-drug interaction phenomenon. Furthermore, we employ a novel network sparsification methodology using the metric backbone of weighted graphs, which reveals the most important edges for inference, recommendation, and visualization, such as pharmacology factors patients discuss on social media. The network sparsification approach also allows us to extract focused digital cohorts from social media whose discourse is more relevant to epilepsy or other biomedical problems. Finally, we present our patient-centered design and pilot-testing of myAURA, including its user interface, based on focus groups and other stakeholder input. Discussion: The ability to search and explore myAURA's heterogeneous data sources via a sparsified multi-layer knowledge graph, as well as the combination of those layers in a single map, are useful features for integrating relevant information for epilepsy. Conclusion: Our stakeholder-driven, scalable approach to integrate traditional and non-traditional data sources, enables biomedical discovery and data-powered patient self-management in epilepsy, and is generalizable to other chronic conditions.

翻译：目的：我们报告了以患者为中心的myAURA应用程序及方法套件的开发，该工具旨在辅助癫痫患者、护理人员及研究者进行照护与自我管理决策。材料与方法：myAURA建立在整合前所未有的癫痫相关异质数据资源（如生物医学数据库、社交媒体及电子健康记录）的联邦架构之上。我们开发了一套可泛化的开源方法论，通过以人为本的生物医学词典的术语计算覆盖所有异质数据的多层知识图谱。结果：该方法的效能首先在药物相互作用现象研究中得到验证。此外，我们采用基于加权图度量骨干网络的新型网络稀疏化方法，揭示了推理、推荐与可视化中最重要的边（例如患者在社交媒体上讨论的药理学因素）。该网络稀疏化方法还可从社交媒体中提取聚焦数字队列，其讨论内容与癫痫或其他生物医学问题更为相关。最终，我们展示了基于焦点小组及其他利益相关者输入的以患者为中心的myAURA设计及用户界面试点测试。讨论：通过稀疏化多层知识图谱检索并探索myAURA异质数据源的能力，以及将这些层级整合至单一地图的特性，是整合癫痫相关信息的关键功能。结论：我们以利益相关者为驱动的可扩展方法整合了传统与非传统数据源，不仅实现了癫痫领域的生物医学发现与数据赋能的患者自我管理，还可泛化应用于其他慢性疾病。