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Title: Discovery of Disease Co-occurrence Patterns from Electronic Healthcare Reimbursement Claims Data

Effective public health surveillance is important for national secu- rity. With novel emerging infectious diseases being reported across different parts of the world, there is a need to build effective bio- surveillance systems that can track, monitor and report such events in a timely manner. Additionally, there is a need to identify sus- ceptible geographic regions/populations where these diseases may have a significant impact and design preemptive strategies to tackle them. With the digitization of health related information through electronic health records (EHR) and electronic healthcare claim re- imbursements (eHCR), there is a tremendous opportunity to ex- ploit these datasets for public health surveillance. In this paper, we present our analysis on the use of eHCR data for bio-surveillance by studying the 2009-2010 H1N1 pandemic flu season. We present a novel approach to extract spatial and temporal patterns of flu in- cidence across the United States (US) from eHCRs and find that a small, but distinct set of break-out patterns govern the flu and asthma incidence rates across the entire country. Further, we ob- serve a distinct temporal lag in the onset of flu when compared to asthma across geographic regions in the US. The patterns extracted from the datamore » collectively indicate how these break-out patterns are coupled, even though the flu represents an infectious disease whereas asthma represents a typical chronic condition. Taken to- gether, our approach demonstrates how mining eHCRs can provide novel insights in tackling public health concerns.« less
 [1] ;  [1] ;  [1] ;  [2] ;  [2] ;  [3]
  1. ORNL
  2. University of Pittsburgh School of Medicine, Pittsburgh PA
  3. IMS Government Solutions (IMSGS), Inc.
Publication Date:
OSTI Identifier:
DOE Contract Number:
Resource Type:
Resource Relation:
Conference: Knowledge Discovery and Data Mining Big Data in Health Informatics (KDD-BHI), New York, NY, USA, 20140824, 20140829
Research Org:
Oak Ridge National Laboratory (ORNL)
Sponsoring Org:
ORNL work for others
Country of Publication:
United States
public health surveillance; data analytics