捷克共和国医疗保健服务中的大数据架构：要求、TPC-H基准和Vertica
Big Data Architecture in Czech Republic Healthcare Service: Requirements, TPC-H Benchmarks and Vertica
摘要：
医疗保健领域的大数据在提高分析能力和降低医疗成本方面发挥了积极作用。除了通过机器学习和数据挖掘算法在支持当前和不久的将来人工智能的平台上提供分析能力外，还需要考虑道德因素，要求采取新的方式来保护隐私，所有这些都是以不断增长的法规和期望为前提的。这项研究的目的是通过为捷克共和国国家卫生服务实施大数据平台来改善现有的临床护理。基于所实现的性能及其对强制性准则的遵守，报告的大数据平台被选为捷克共和国国家招标（招标编号VZ 0036628，编号Z2017-035520）的获胜解决方案。该平台基于用于海量数据处理的Vertica NoSQL分析数据库，符合TPC-H1决策支持基准、欧盟（EU）和捷克共和国的要求，远远超过了定义的系统性能阈值。报告的人工制品和概念可转移到其他国家的医疗保健系统，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。所实施的平台允许：（1）可扩展性;（2）进一步实施新开发的用于分类和预测分析的机器学习算法;（3）通过使用数据加密和解密的自动化功能来改进与电子健康记录（EHR）相关的安全性;以及（4）使用大数据来实现医疗保健中的战略规划。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
