Accepted for/Published in: Journal of Medical Internet Research
Date Submitted: Dec 6, 2018
Open Peer Review Period: Dec 10, 2018 - Dec 26, 2018
Date Accepted: Jan 13, 2019
(closed for review but you can still tweet)
A Scalable Data Science Platform for Healthcare and Precision Medicine Research
ABSTRACT
Background:
Healthcare data is increasing in volume and complexity. Storing and analyzing this 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 healthcare and designed for scalability and growth.
Objective:
To (1) demonstrate the implementation of a data science platform built on open-source technology within a large, academic healthcare system and (2) describe two computational healthcare applications built on such a platform.
Methods:
A data science platform based on several open source technologies was deployed to support real-time, big data workloads. Data acquisition workflows for Apache Storm and NiFi were developed in Java and Python to capture patient monitoring and laboratory data for downstream analytics.
Results:
The use of 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 data sets. This infrastructure also provides a robust analytics platform where healthcare and biomedical research data can be analyzed in near real-time for precision medicine and computational healthcare use cases.
Conclusions:
The implementation and use of integrated data science platforms offer organizations the opportunity to combine traditional data sets, 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 healthcare data for computational healthcare and precision medicine research.
Citation
Per the author's request the PDF is not available.
Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.