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Leverage hadoop framework for large scale clinical informatics applications.

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This study details using the Apache Hadoop framework for big data in clinical informatics. It offers best practices for processing diverse data, performing data mining, and enabling patient-centric modeling for research.

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Area of Science:

  • Clinical Informatics
  • Data Science
  • Computational Biology

Background:

  • Clinical informatics faces challenges with high data volume and complex computations.
  • Integrating diverse and heterogeneous data sources is crucial for translational research.
  • Existing frameworks may not adequately address the 'Big Data' needs in healthcare.

Purpose of the Study:

  • To present experiences and best practices for using the Apache Hadoop framework in clinical informatics.
  • To outline a scalable solution for processing and analyzing large, complex datasets in a clinical research setting.
  • To demonstrate the application of Hadoop, Mahout, and HBase for advanced data analysis and patient modeling.

Main Methods:

  • Utilizing standard Hadoop tools and custom MapReduce programs for data integration from diverse sources.
  • Employing the Mahout data mining library for fine-grained aggregate data analysis.
  • Leveraging HBase's column-oriented features for patient-centric modeling and temporal reasoning.

Main Results:

  • A scalable framework was developed to handle high-volume, computationally intensive applications in clinical informatics.
  • The framework successfully integrates diverse data, performs data mining, and supports complex patient modeling.
  • Demonstrated the utility of Apache Hadoop, Mahout, and HBase for clinical and translational research.

Conclusions:

  • The Apache Hadoop framework offers a scalable, fault-tolerant, and highly available solution for 'Big Data' challenges in clinical informatics.
  • Best practices were established for data integration, analysis, and modeling using Hadoop ecosystem tools.
  • This approach facilitates enterprise-level deployment for clinical and translational research, meeting growing data demands.