Work2016

Interactive Healthcare Big Data Analytics Platform under Simulated Performance

Dillon Chrimes · Belaid Moa · Hamid Zamani · Mu-Hsing Kuo

12 citations0/0 files storedIEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech)

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Big Data Technologies and Applications Electronic Health Records Systems Artificial Intelligence in Healthcare

Abstract

To utilize data from hospital systems, big data analytics (BDA) has become increasingly important. BDA enable queries of large highly diverse and real volumes of patient data in an interactively dynamic way that enriches the use of the platform with data visualization for healthcare. We established a Healthcare BDA (HBDA) platform at the University of Victoria (UVic) with Compute Canada/Westgrid, and Vancouver Island Health Authority (VIHA), Victoria, BC, Canada. The framework was a proof-of-concept implementation that tested emulated patient data representative of the main hospital system at VIHA. We cross-referenced all data, its profiles and metadata, with the existing clinical reporting. Our HBDA platform and its performance was tested for different patient query types in simulation with the data ingested into Hadoop file system over different applications of Apache Spark with Zeppelin and Jupyter web-based interfaces, and Apache Drill interfaces. The results showed that the ingestion time of one billion records took circa 2 hours via Apache Spark. Apache Drill outperformed Spark/Zeppelin and Spark/Jupyter. However, it was restricted to running more simplified queries, and very limited in its visualizations exhibiting poor usability for healthcare. Zeppelin running on Spark showed ease-of-use interactions for health applications, but it lacked the flexibility of its interface tools and required extra setup time before running queries. Jupyter on Spark offered high performance stacks not only over our HBDA platform but also in unison to run all queries simultaneously with high usability for a variety of reporting requirements by providers and health professionals.

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References

2012Business Intelligence and Analytics: From Big Data to Big Impact 2011Big data: The next frontier for innovation, competition, and productivity 2012Challenges and opportunities with big data 2012Leveraging the capabilities of service-oriented decision support systems: Putting analytics and big data in cloud 2014Big Data in Science and Healthcare: A Review of Recent Literature and Perspectives 2014Technical Challenges for Big Data in Biomedicine and Health: Data Sources, Infrastructure, and Analytics 2015Design and Construction of a Big Data Analytics Framework for Health Applications 2014Big Data Analytics using Hadoop

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