A Scalable Data Science Platform for Healthcare and Precision Medicine Research

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.

[1]  Jimeng Sun,et al.  Big data analytics for healthcare , 2013, KDD.

[2]  Aditya B. Patel,et al.  Addressing big data problem using Hadoop and Map Reduce , 2012, 2012 Nirma University International Conference on Engineering (NUiCONE).

[3]  Ehud Gudes,et al.  Security and privacy aspects in MapReduce on clouds: A survey , 2016, Comput. Sci. Rev..

[4]  J. Manyika.  Big data: The next frontier for innovation, competition, and productivity , 2011 .

[5]  Amir Bashan,et al.  Network physiology reveals relations between network topology and physiological function , 2012, Nature Communications.

[6]  Mario Plebani,et al.  Errors in laboratory medicine. , 2002, Clinical chemistry.

[7]  Priya P. Sharma.  Securing Big Data Hadoop : A Review of Security Issues , Threats and Solution , 2014 .

[8]  Les Jordan,et al.  The problem with Big Data in Translational Medicine. A review of where we've been and the possibilities ahead , 2015, Applied & translational genomics.

[9]  Yash Punjabi,et al.  SECURITY ISSUES ASSOCIATED WITH BIG DATA IN CLOUD COMPUTING , 2017 .

[10]  M. P. Griffin,et al.  Toward the early diagnosis of neonatal sepsis and sepsis-like illness using novel heart rate analysis. , 2001, Pediatrics.

[11]  D. Longo,et al.  Precision medicine--personalized, problematic, and promising. , 2015, The New England journal of medicine.

[12]  Natasa Przulj,et al.  Integrative methods for analyzing big data in precision medicine , 2016, Proteomics.

[13]  Shiming Yang,et al.  Automated continuous vital signs predict use of uncrossed matched blood and massive transfusion following trauma , 2016, The journal of trauma and acute care surgery.

[14]  Scott Shenker,et al.  Spark: Cluster Computing with Working Sets , 2010, HotCloud.

[15]  Michael J. Franklin,et al.  Resilient Distributed Datasets: A Fault-Tolerant Abstraction for In-Memory Cluster Computing , 2012, NSDI.

[16]  Jonathan Seidman,et al.  Hadoop Application Architectures , 2015 .

[17]  Huang Fang.  Managing data lakes in big data era: What's a data lake and why has it became popular in data management ecosystem , 2015, 2015 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER).

[18]  Muthu Dayalan,et al.  MapReduce : Simplified Data Processing on Large Cluster , 2018 .

[19]  F. Collins,et al.  A new initiative on precision medicine. , 2015, The New England journal of medicine.

[20]  Yuan Yu,et al.  Dryad: distributed data-parallel programs from sequential building blocks , 2007, EuroSys '07.

[21]  Sanjay Ghemawat,et al.  MapReduce: Simplified Data Processing on Large Clusters , 2004, OSDI.