Health Care and Precision Medicine Research: Analysis of a Scalable Data Science Platform
Health Care and Precision Medicine Research: Analysis of a Scalable Data Science Platform
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DOI：
10.2196/13043
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发表时间：
2019-04-09
影响因子：
7.4
通讯作者：
Schulz, Wade L.
中科院分区：
文献类型：
--
作者：
McPadden, Jacob;Durant, Thomas J. S.;Schulz, Wade L.
背景：医疗保健数据的数量和复杂性都在增加。存储和分析这些数据以实施精确医学倡议和数据驱动的研究已经超出了传统计算机系统的能力。现代大数据平台必须适应医疗保健的特定需求，并为可伸缩性和增长而设计。目的：(1)展示基于开源技术的数据科学平台在大型学术医疗系统中的实施；(2)描述在此平台上构建的2个计算医疗保健应用。方法：我们部署了一个基于多种开源技术的数据科学平台，以支持实时的大数据工作负载。我们使用Java和Python语言开发了用于ApacheStorm和NiFi的数据采集工作流，以捕获患者监测和实验室数据以供下游分析。结果：新兴的数据管理方法以及Hadoop等开源技术可用于创建集成的数据湖，以存储大型实时数据集。该基础设施还提供了一个强大的分析平台，可在此平台上近乎实时地分析医疗保健和生物医学研究数据，以用于精确医学和计算医疗保健使用案例。结论：集成数据科学平台的实施和使用为组织提供了将传统数据集(包括来自电子健康记录的数据)与新兴大数据来源(如患者连续监测和实时实验室结果)相结合的机会。这些平台可以实现对信息的经济高效和可扩展的分析，这些信息将是交付精确医疗计划的关键。能够利用数据科学平台中发现的技术进步的组织将有机会为计算医疗保健和精确医学研究提供全面的医疗数据访问。
Background: Health care data are increasing in volume and complexity. Storing and analyzing these data to implement precision medicine initiatives and data-driven research has exceeded the capabilities of traditional computer systems. Modern big data platforms must be adapted to the specific demands of health care and designed for scalability and growth.Objective: The objectives of our study were to (1) demonstrate the implementation of a data science platform built on open source technology within a large, academic health care system and (2) describe 2 computational health care applications built on such a platform.Methods: We deployed a data science platform based on several open source technologies to support real-time, big data workloads. We developed data-acquisition workflows for Apache Storm and NiFi in Java and Python to capture patient monitoring and laboratory data for downstream analytics.Results: Emerging data management approaches, along with open source technologies such as Hadoop, can be used to create integrated data lakes to store large, real-time datasets. This infrastructure also provides a robust analytics platform where health care and biomedical research data can be analyzed in near real time for precision medicine and computational health care use cases.Conclusions: The implementation and use of integrated data science platforms offer organizations the opportunity to combine traditional datasets, including data from the electronic health record, with emerging big data sources, such as continuous patient monitoring and real-time laboratory results. These platforms can enable cost-effective and scalable analytics for the information that will be key to the delivery of precision medicine initiatives. Organizations that can take advantage of the technical advances found in data science platforms will have the opportunity to provide comprehensive access to health care data for computational health care and precision medicine research.