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J Biomed Inform. 2014 Apr;48:160-70. doi: 10.1016/j.jbi.2013.12.012. Epub 2013 Dec 25.

PARAMO: a PARAllel predictive MOdeling platform for healthcare analytic research using electronic health records.

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IBM TJ Watson Research Center, Yorktown Heights, NY, United States. Electronic address:
IBM TJ Watson Research Center, Yorktown Heights, NY, United States.
Scripps Translational Science Institute, LaJolla, CA, United States; Geisinger Medical Center, Danville, PA, United States.
Sutter Health, Concord, CA, United States.
Department of Biomedical Informatics, School of Medicine, Vanderbilt University, Nashville, TN, United States; Department of Electrical Engineering and Computer Science, School of Engineering, Vanderbilt University, Nashville, TN, United States.



Healthcare analytics research increasingly involves the construction of predictive models for disease targets across varying patient cohorts using electronic health records (EHRs). To facilitate this process, it is critical to support a pipeline of tasks: (1) cohort construction, (2) feature construction, (3) cross-validation, (4) feature selection, and (5) classification. To develop an appropriate model, it is necessary to compare and refine models derived from a diversity of cohorts, patient-specific features, and statistical frameworks. The goal of this work is to develop and evaluate a predictive modeling platform that can be used to simplify and expedite this process for health data.


To support this goal, we developed a PARAllel predictive MOdeling (PARAMO) platform which (1) constructs a dependency graph of tasks from specifications of predictive modeling pipelines, (2) schedules the tasks in a topological ordering of the graph, and (3) executes those tasks in parallel. We implemented this platform using Map-Reduce to enable independent tasks to run in parallel in a cluster computing environment. Different task scheduling preferences are also supported.


We assess the performance of PARAMO on various workloads using three datasets derived from the EHR systems in place at Geisinger Health System and Vanderbilt University Medical Center and an anonymous longitudinal claims database. We demonstrate significant gains in computational efficiency against a standard approach. In particular, PARAMO can build 800 different models on a 300,000 patient data set in 3h in parallel compared to 9days if running sequentially.


This work demonstrates that an efficient parallel predictive modeling platform can be developed for EHR data. This platform can facilitate large-scale modeling endeavors and speed-up the research workflow and reuse of health information. This platform is only a first step and provides the foundation for our ultimate goal of building analytic pipelines that are specialized for health data researchers.


Electronic health records; Map reduce; Parallel computing; Predictive modeling; Scientific workflows

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