论文标题
捷克共和国医疗保健服务的大数据架构：要求，TPC-H基准和Vertica
Big Data Architecture in Czech Republic Healthcare Service: Requirements, TPC-H Benchmarks and Vertica
论文作者
论文摘要
医疗保健中的大数据在提高分析能力并降低医疗成本方面产生了积极的影响。除了在平台上提供分析能力，以支持当前和近未实现的AI，并提供机器学习和数据挖掘算法，还需要道德考虑，要求将新的方法保留隐私，所有这些方法都由法规和期望的日益增长的体系进行了预言。这项研究的目的是通过为捷克共和国国家卫生局实施大数据平台来改善现有的临床护理。根据实现的绩效及其遵守强制性准则，据报道的Big-Data平台被选为捷克共和国国家招标的获胜解决方案（招标ID。VZ0036628，No.Z2017-035520）。该平台基于用于大规模数据处理的分析Vertica NOSQL数据库，它符合TPC-H1的决策支持基准，欧盟（EU）和捷克共和国的要求，并确定了确定的系统性能阈值。报告的工件和概念可将其他国家的医疗保健系统转移到医疗系统中，并旨在以具有成本效益，可扩展和高性能的方式从大数据中提供个性化的自主评估。实现的平台允许：（1）可伸缩性； （2）进一步实施用于分类和预测分析的新开发的机器学习算法； （3）通过使用自动化功能进行数据加密和解密，与电子健康记录（EHR）相关的安全改进； （4）使用大数据允许在医疗保健中进行战略计划。
Big data in healthcare has made a positive difference in advancing analytical capabilities and lowering the costs of medical care. In addition to providing analytical capabilities on platforms supporting current and near-future AI with machine-learning and data-mining algorithms, there is also a need for ethical considerations mandating new ways to preserve privacy, all of which are preconditioned by the growing body of regulations and expectations. The purpose of this study is to improve existing clinical care by implementing a big data platform for the Czech Republic National Health Service. Based on the achieved performance and its compliance with mandatory guidelines, the reported big-data platform was selected as the winning solution from the Czech Republic national tender (Tender Id. VZ0036628, No. Z2017-035520). The platform, based on analytical Vertica NoSQL database for massive data processing, complies with the TPC-H1 for decision support benchmark, the European Union (EU) and the Czech Republic requirements, well-exceeding defined system performance thresholds. The reported artefacts and concepts are transferrable to healthcare systems in other countries and are intended to provide personalised autonomous assessment from big data in a cost-effective, scalable and high-performance manner. The implemented platform allows: (1) scalability; (2) further implementations of newly-developed machine learning algorithms for classification and predictive analytics; (3) security improvements related to Electronic Health Records (EHR) by using automated functions for data encryption and decryption; and (4) the use of big data to allow strategic planning in healthcare.
