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

https://doi.org/10.1016/j.jbi.2013.12.012Get rights and content
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Highlights

  • A predictive modeling platform that explores many predictive models in parallel.

  • The platform constructs a dependency graph of tasks from a set of model specifications.

  • The platform schedules the tasks and executes them in parallel using Map-Reduce.

  • Performance is assessed using real EHR data sets with up to 300,000 patients.

  • Significant performance speedups in model building and evaluation are achieved.

Abstract

Objective

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.

Methods

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.

Results

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 3 h in parallel compared to 9 days if running sequentially.

Conclusion

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.

Keywords

Predictive modeling
Electronic health records
Scientific workflows
Parallel computing
Map reduce

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