Methodological challenges and analytic opportunities for modeling and interpreting Big Healthcare Data.
Methodological challenges and analytic opportunities for modeling and interpreting Big Healthcare Data.
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方法论上的挑战和分析机会，用于建模和解释大型医疗保健数据。
DOI：
10.1186/s13742-016-0117-6
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发表时间：
2016
期刊：
影响因子：
9.2
通讯作者：
Dinov ID
中科院分区：
文献类型：
--
作者：
Dinov ID
管理、处理和理解医疗保健大数据具有挑战性、成本高昂且要求较高。如果没有强大的表示、分析和推理基础理论，统一处理和分析此类复杂数据的路线图仍然难以实现。在本文中，我们概述了用于混合复杂医疗数据、高级分析工具和分布式科学计算的各种大数据挑战、机遇、建模方法和软件技术。使用成像、遗传和医疗保健数据，我们提供了使用分布式云服务、自动化和半自动化分类技术以及开放科学协议处理异构数据集的示例。尽管取得了巨大进步，但仍需要开发新的创新技术来增强、扩展和优化大型、复杂和异构数据的管理和处理。利益相关者在数据获取、研发、计算基础设施和教育方面的投资对于实现大数据的巨大潜力、获得预期的信息效益和建立持久的知识资产至关重要。多方面的专有、开源和社区开发对于实现广泛、可靠、可持续和高效的数据驱动发现和分析至关重要。大数据将影响经济的各个领域，其标志是“团队科学”。
Managing, processing and understanding big healthcare data is challenging, costly and demanding. Without a robust fundamental theory for representation, analysis and inference, a roadmap for uniform handling and analyzing of such complex data remains elusive. In this article, we outline various big data challenges, opportunities, modeling methods and software techniques for blending complex healthcare data, advanced analytic tools, and distributed scientific computing. Using imaging, genetic and healthcare data we provide examples of processing heterogeneous datasets using distributed cloud services, automated and semi-automated classification techniques, and open-science protocols. Despite substantial advances, new innovative technologies need to be developed that enhance, scale and optimize the management and processing of large, complex and heterogeneous data. Stakeholder investments in data acquisition, research and development, computational infrastructure and education will be critical to realize the huge potential of big data, to reap the expected information benefits and to build lasting knowledge assets. Multi-faceted proprietary, open-source, and community developments will be essential to enable broad, reliable, sustainable and efficient data-driven discovery and analytics. Big data will affect every sector of the economy and their hallmark will be ‘team science’.