Big Data Analytics in Healthcare Systems

Lidong Wang,Cheryl Ann Alexander

doi:10.33889/ijmems.2019.4.1-002

Abstract

Big Data analytics can improve patient outcomes, advance and personalize care, improve provider relationships with patients, and reduce medical spending. This paper introduces healthcare data, big data in healthcare systems, and applications and advantages of Big Data analytics in healthcare. We also present the technological progress of big data in healthcare, such as cloud computing and stream processing. Challenges of Big Data analytics in healthcare systems are also discussed.

Highlights

Matching patients with medical treatments for specific diseases can reduce unnecessary side-effects, improve the treatment quality, and avoid improper treatment or waste in medical services
The value hidden in an isolated data source may be limited, but the deep value could be maximized from healthcare data through the data fusion of electronic medical records(EMRs) and electronic health records (EHRs) (Zhang et al, 2017)
Precision medicine is a kind of Big Data application in health, which benefit from multi-omics, IoT, Industry 4.0, etc

Summary

Introduction

Matching patients with medical treatments for specific diseases can reduce unnecessary side-effects, improve the treatment quality, and avoid improper treatment or waste in medical services. Description Internet search, Smartphone, purchase, HER, healthcare services, IoT sensors, medical and fitness device Clinical and other data related to health in identified forms which are collected, stored, and distributed to third parties. Analyzing tweets in healthcare has the potential to change the way people and healthcare providers use advanced technologies to achieve new clinical insights (Cunha et al, 2015) Open sources such as Hadoop, Kafka, Apache Storm, and NoSQL Cassandra have been used in Big Data analytics. The procedures include 1) use parallel large-scale rough set methods for feature acquisition and implement them on MapReduce runtime systems such as Twister, Phoenix and Hadoop to obtain features from big datasets through data mining; 2) use the framework structure of < key, value > pair to accelerate the computation of equivalence classes and attribute significance; parallelize traditional attribute reduction process based on MapReduce (Ding et al, 2018).

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