Proto-OKN Theme 1: A Dynamically-Updated Open Knowledge Network for Health: Integrating Biomedical Insights with Social Determinants of Health

该项目旨在通过纳入健康的社会决定因素 (SDoH) 数据来扩展当前的生物医学知识图，尽管这些数据已被证明与健康结果相关，但通常未被充分利用。目标是创建一个综合的健康知识图，结合生物医学事实和科学文献和电子健康记录中的 SDoH 数据。至关重要的是，该模型将允许持续更新生物医学和临床文本数据流，促进新研究成果的快速分发，并促进不断发展的生物医学和公共卫生领域的合作。生物医学知识图（KG）负责组织、分析和应用生物医学信息，很少纳入与社会经济地位、教育和就业等非临床因素相关的数据，而这些数据已被证明与多种健康结果相关。将 SDoH 纳入开放知识库对于加强健康改善工作并减少医疗资源可及性方面持续存在的差异至关重要。这种整合的成果之一将是建立一个将生物医学事实与 SDoH 数据相融合的综合健康知识网络。该网络可以不断地用新数据进行自我更新。这项工作面临的挑战包括数据集成、数据流的动态性质以及以公平和道德的方式将生物医学知识与 SDoH 数据结合起来的需要。为了应对这些挑战，该项目将创建一个多维知识网络，能够支持跨各种应用程序的复杂查询并自动验证信息质量。该项目还将把知识库与电子健康记录连接起来，以揭示社会决定因素和健康结果之间的关联，从而改善医疗保健结果并促进健康公平。该项目通过推进语义互操作性、改进知识表示、实现自适应知识获取、确保知识图谱可信度和提升道德意识来进行创新。它旨在产生一个集成 SDoH 事实和数据的综合知识网络、开源算法、模型和工具。该项目创建的工具的可访问性将加速知识发现，增进对人类健康和弱势群体福祉的理解。此外，该项目将通过特别研讨会课程和持续的多元化举措，为学生的教育做出重大贡献，特别是那些来自弱势群体的学生。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优点和更广泛的影响审查标准进行评估，被认为值得支持。

This project aims to expand current biomedical knowledge graphs by incorporating Social Determinants of Health (SDoH) data, which are typically underutilized despite their proven connection to health outcomes. The goal is to create a comprehensive health knowledge graph that combines biomedical facts and SDoH data from scientific literature and Electronic Health Records. Crucially, this model will allow for continuous updates from ongoing streams of biomedical and clinical text data, promoting swift distribution of new research findings and fostering collaboration in the ever-evolving biomedical and public health fields. Biomedical knowledge graphs (KGs), which organize, analyze, and apply biomedical information, rarely incorporate data related to non-clinical factors like socioeconomic status, education, and employment, that have proven correlations with several health outcomes. It is crucial to integrate SDoH into open knowledge repositories to enhance health improvement efforts and to reduce persistent disparities in healthcare resource accessibility. One outcome of this integration would be a comprehensive knowledge network for health that merges biomedical facts with SDoH data. This network could continually update itself with new data. Challenges to this endeavor include data integration, the dynamic nature of data streams, and the need to combine biomedical knowledge with SDoH data in an equitable and ethical manner. To address these challenges, the project will create a multi-dimensional knowledge network capable of supporting complex queries across various applications and automatically verifying information quality. The project will also link the KGs with electronic health records to uncover associations between social determinants and health outcomes, thereby improving healthcare outcomes and promoting health equity. This project innovates by advancing semantic interoperability, improving knowledge representation, enabling adaptive knowledge acquisition, ensuring knowledge graph trustworthiness, and promoting ethical awareness. It aims to yield a comprehensive knowledge network, open-source algorithms, models, and tools that integrate SDoH facts and data. The accessibility of the tools created in this project will accelerate knowledge discovery, improving the understanding of human health and the wellbeing of vulnerable populations. Additionally, this project will significantly contribute to the education of students, particularly those from underrepresented groups, through special seminar courses and ongoing diversity initiatives.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.