Abstract：
:The increasing availability of electronic health care records has provided remarkable progress in the field of population health. In particular the identification of disease risk factors has flourished under the surge of available data. Researchers can now access patient data across a broad range of demographics and geographic locations. Utilizing this Big healthcare data researchers have been able to empirically identify specific high-risk conditions found within differing populations. However to date the majority of studies approached the issue from the top down, focusing on the prevalence of specific diseases within a population. Through our work we demonstrate the power of addressing this issue bottom-up by identifying specifically which diseases are higher-risk for a specific population. In this work we demonstrate that network-based analysis can present a foundation to identify pairs of diagnoses that differentiate across population segments. We provide a case study highlighting differences between high and low income individuals in the United States. This work is particularly valuable when addressing population health management within resource-constrained environments such as community health programs where it can be used to provide insight and resource planning into targeted care for the population served.
journal_name
Sci Repjournal_title
Scientific reportsauthors
Feldman K,Stiglic G,Dasgupta D,Kricheff M,Obradovic Z,Chawla NVdoi
10.1038/srep30465subject
Has Abstractpub_date
2016-07-27 00:00:00pages
30465issn
2045-2322pii
srep30465journal_volume
6pub_type
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