A Scalable Data Science Platform for Healthcare and Precision Medicine Research
Abstract
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. Materials and 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. Discussion: 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. Conclusion: 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.
Keywords
Cite
@article{arxiv.1808.04849,
title = {A Scalable Data Science Platform for Healthcare and Precision Medicine Research},
author = {Jacob McPadden and Thomas JS Durant and Dustin R Bunch and Andreas Coppi and Nathan Price and Kris Rodgerson and Charles J Torre and William Byron and H Patrick Young and Allen L Hsiao and Harlan M Krumholz and Wade L Schulz},
journal= {arXiv preprint arXiv:1808.04849},
year = {2018}
}
Comments
8 pages, 4 figures, 1 table