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

Tópicos

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Resumo

JECTIVE: 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 3h in parallel compared to 9days 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.

Resumo Limpo

jectiv healthcar analyt research increas involv construct predict model diseas target across vari patient cohort use electron health record ehr facilit process critic support pipelin task cohort construct featur construct crossvalid featur select classif develop appropri model necessari compar refin model deriv divers cohort patientspecif featur statist framework goal work develop evalu predict model platform can use simplifi expedit process health datamethod support goal develop parallel predict model paramo platform construct depend graph task specif predict model pipelin schedul task topolog order graph execut task parallel implement platform use mapreduc enabl independ task run parallel cluster comput environ differ task schedul prefer also supportedresult assess perform paramo various workload use three dataset deriv ehr system place geising health system vanderbilt univers medic center anonym longitudin claim databas demonstr signific gain comput effici standard approach particular paramo can build differ model patient data set h parallel compar day run sequentiallyconclus work demonstr effici parallel predict model platform can develop ehr data platform can facilit largescal model endeavor speedup research workflow reus health inform platform first step provid foundat ultim goal build analyt pipelin special health data research

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