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Human Disease Network: A Study Based...

Liu, Xinchun.

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Human Disease Network: A Study Based on Taiwan National Health Insurance Research Database.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Human Disease Network: A Study Based on Taiwan National Health Insurance Research Database./
Author:	Liu, Xinchun.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2017,
Description:	50 p.
Notes:	Source: Masters Abstracts International, Volume: 79-10.
Contained By:	Masters Abstracts International79-10.
Subject:	Biostatistics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10276775
ISBN:	9780355777383

Human Disease Network: A Study Based on Taiwan National Health Insurance Research Database.
Liu, Xinchun.

Human Disease Network: A Study Based on Taiwan National Health Insurance Research Database. - Ann Arbor : ProQuest Dissertations & Theses, 2017 - 50 p.

Source: Masters Abstracts International, Volume: 79-10.

Thesis (M.P.H.)--Yale University, 2017.

This item must not be added to any third party search indexes.

Many of the existing resources from genetic perspective have been constructed to help understand the origins of many diseases. While progress on genetic fronts has been impressive, many of these sources overlook the information we could get from analyzing patient clinical histories. Our primary goal here is to define the human disease network using epidemiological data from a population science perspective and detect pairwise co-morbidity correlations. Network analysis is increasingly used to explore the co-occurrence of human diseases. We here employed a network analysis approach, called weighted correlation network analysis (WGCNA), to find significant associations among over 600 diseases in over 800,000 patients from Taiwan National Health Insurance Research Database through year 2000 to 2013. The concepts of network construction is straightforward: nodes represent diseases and edges represent the connections between nodes. Nodes are connected if the corresponding diseases has the high possibility of being comorbid. We detect co-morbidity of disease by (1) calculating pairwise connections; (2) identifying diseases with high comorbidity rate; (3) exploring diseases clustering and its changing pattern over fourteen years. Our findings show that Diabetes mellitus, Disorders of lipoid metabolism and eight other diseases have the highest possibility to co-occur with other diseases. And these ten diseases constantly have high risk of being comorbid with other diseases. Our clustering results show diseases within same category tends to occur together. In addition, diseases share same risk factors but in different categories may also appear simultaneously.

ISBN: 9780355777383Subjects--Topical Terms:

1002712
Biostatistics.

Human Disease Network: A Study Based on Taiwan National Health Insurance Research Database.
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500 $a Advisor: Ma, Shuangge.
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506 $a This item must not be added to any third party search indexes.
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520 $a Many of the existing resources from genetic perspective have been constructed to help understand the origins of many diseases. While progress on genetic fronts has been impressive, many of these sources overlook the information we could get from analyzing patient clinical histories. Our primary goal here is to define the human disease network using epidemiological data from a population science perspective and detect pairwise co-morbidity correlations. Network analysis is increasingly used to explore the co-occurrence of human diseases. We here employed a network analysis approach, called weighted correlation network analysis (WGCNA), to find significant associations among over 600 diseases in over 800,000 patients from Taiwan National Health Insurance Research Database through year 2000 to 2013. The concepts of network construction is straightforward: nodes represent diseases and edges represent the connections between nodes. Nodes are connected if the corresponding diseases has the high possibility of being comorbid. We detect co-morbidity of disease by (1) calculating pairwise connections; (2) identifying diseases with high comorbidity rate; (3) exploring diseases clustering and its changing pattern over fourteen years. Our findings show that Diabetes mellitus, Disorders of lipoid metabolism and eight other diseases have the highest possibility to co-occur with other diseases. And these ten diseases constantly have high risk of being comorbid with other diseases. Our clustering results show diseases within same category tends to occur together. In addition, diseases share same risk factors but in different categories may also appear simultaneously.
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