title: Cloud-based Predictive Modeling System and its Application to Asthma Readmission Prediction.
abstract: The predictive modeling process is time consuming and requires clinical researchers to handle complex electronic health record (EHR) data in restricted computational environments. To address this problem, we implemented a cloud-based predictive modeling system via a hybrid setup combining a secure private server with the Amazon Web Services (AWS) Elastic MapReduce platform. EHR data is preprocessed on a private server and the resulting de-identified event sequences are hosted on AWS. Based on user-specified modeling configurations, an on-demand web service launches a cluster of Elastic Compute 2 (EC2) instances on AWS to perform feature selection and classification algorithms in a distributed fashion. Afterwards, the secure private server aggregates results and displays them via interactive visualization. We tested the system on a pediatric asthma readmission task on a de-identified EHR dataset of 2,967 patients. We conduct a larger scale experiment on the CMS Linkable 2008-2010 Medicare Data Entrepreneurs' Synthetic Public Use File dataset of 2 million patients, which achieves over 25-fold speedup compared to sequential execution.
authors.affiliation: School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, GA, USA.
authors.forename: Robert
authors.initials: R
authors.lastname: Chen
authors.affiliation: School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, GA, USA.
authors.forename: Hang
authors.initials: H
authors.lastname: Su
authors.affiliation: School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, GA, USA.
authors.forename: Mohammed
authors.initials: M
authors.lastname: Khalilia
authors.affiliation: School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, GA, USA.
authors.forename: Sizhe
authors.initials: S
authors.lastname: Lin
authors.affiliation: School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, GA, USA.
authors.forename: Yue
authors.initials: Y
authors.lastname: Peng
authors.affiliation: Children's Healthcare of Atlanta, Atlanta, GA, USA.
authors.forename: Tod
authors.initials: T
authors.lastname: Davis
authors.affiliation: Pediatric Emergency Medicine Associates, LLC, Atlanta, GA, USA.
authors.forename: Daniel A
authors.initials: DA
authors.lastname: Hirsh
authors.affiliation: Children's Healthcare of Atlanta, Atlanta, GA, USA.
authors.forename: Elizabeth
authors.initials: E
authors.lastname: Searles
authors.affiliation: Children's Healthcare of Atlanta, Atlanta, GA, USA.
authors.forename: Javier
authors.initials: J
authors.lastname: Tejedor-Sojo
authors.affiliation: Children's Healthcare of Atlanta, Atlanta, GA, USA.
authors.forename: Michael
authors.initials: M
authors.lastname: Thompson
authors.affiliation: School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, GA, USA.
authors.forename: Jimeng
authors.initials: J
authors.lastname: Sun
mesh_terms: D001249:Asthma / Q000628:therapy; D000067917:Cloud Computing; D019295:Computational Biology; D003198:Computer Simulation; D057286:Electronic Health Records / Q000458:organization & administration*; D005544:Forecasting; D006801:Humans; D008954:Models, Biological; D010359:Patient Readmission; D011379:Prognosis