Paper Info
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.
Year
Venue
Field
2015
AMIA
Data mining,Feature selection,Computer science,Real-time computing,Interactive visualization,Amazon web services,Statistical classification,Web service,Speedup,Cloud computing
DocType
Volume
Citations 
Conference
2015
3
PageRank 
References 
Authors
0.42
7
12
Name
Order
Citations
PageRank
Robert Chen110411.21
Hang Su2107.98
Yi Zhen331.77
Mohammed Khalilia4202.45
Daniel Hirsh571.12
Michael S. Thompson68014.53
Tod Davis730.42
Yue Peng8122.53
Sizhe Lin931.77
Javier Tejedor-Sojo1030.76
Elizabeth Searles11783.75
Jimeng Sun124729240.91