基于数据桥的生物医学异构数据集成与等级检索

P. Deshpande
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引用次数: 1

摘要

数字化世界要求数据集成系统将来自多个数据源的数据存储库组合在一起。大量的临床和生物医学研究数据被认为是推动健康研究和提高医疗保健服务效率的数据驱动研究的主要力量。数据驱动的研究可能有许多目标,包括但不限于改进诊断过程、新的生物医学发现、流行病学和教育。然而,寻找和获取相关数据仍然是一个难以实现的目标。我们确定了这些挑战,并开发了一个集成放射学图像搜索(IRIS)框架,这可能是帮助数据驱动研究的一步。我们建议构建数据桥来支持从集成存储库中检索排序的相关文档。我的研究重点是生物医学数据集成和索引系统,并提供从集成存储库中排序的文档检索。虽然我们目前的重点是整合生物医学数据源(为医疗专业人员),但我们相信,我们提出的框架和方法也可以用于其他领域。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Biomedical Heterogeneous Data Integration and Rank Retrieval using Data Bridges
Digitized world demands data integration systems that combine data repositories from multiple data sources. Vast amounts of clinical and biomedical research data are considered a primary force enabling data-driven research toward advancing health research and for introducing efficiencies in healthcare delivery. Data-driven research may have many goals, including but not limited to improved diagnostics processes, novel biomedical discoveries, epidemiology, and education. However, finding and gaining access to relevant data remains an elusive goal. We identified these challenges and developed an Integrated Radiology Image Search (IRIS) framework that could be a step toward aiding data-driven research. We propose building data bridges to support retrieving ranked relevant documents from integrated repository. My research focuses on biomedical data integration and indexing systems and provide ranked document retrieval from an integrated repository. Though we currently focus on integrating biomedical data sources (for medical professionals), we believe that our proposed framework and methodologies can be used in other domains as well.
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