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Patient Populations, Clinical Associ...

Liu, Yazhuo.

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Patient Populations, Clinical Associations, and System Efficiency in Healthcare Delivery System.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Patient Populations, Clinical Associations, and System Efficiency in Healthcare Delivery System./
作者:	Liu, Yazhuo.
面頁冊數:	87 p.
附註:	Source: Dissertation Abstracts International, Volume: 76-11(E), Section: B.
Contained By:	Dissertation Abstracts International76-11B(E).
標題:	Industrial engineering. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3714134
ISBN:	9781321912159

Patient Populations, Clinical Associations, and System Efficiency in Healthcare Delivery System.
Liu, Yazhuo.

Patient Populations, Clinical Associations, and System Efficiency in Healthcare Delivery System. - 87 p.

Source: Dissertation Abstracts International, Volume: 76-11(E), Section: B.

Thesis (Ph.D.)--University of South Florida, 2015.

The efforts to improve health care delivery usually involve studies and analysis of patient populations and healthcare systems. In this dissertation, I present the research conducted in the following areas: identifying patient groups, improving treatments for specific conditions by using statistical as well as data mining techniques, and developing new operation research models to increase system efficiency from the health institutes' perspective. The results provide better understanding of high risk patient groups, more accuracy in detecting disease' correlations and practical scheduling tools that consider uncertain operation durations and real-life constraints.

ISBN: 9781321912159Subjects--Topical Terms:

526216
Industrial engineering.

Patient Populations, Clinical Associations, and System Efficiency in Healthcare Delivery System.
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520 $a The efforts to improve health care delivery usually involve studies and analysis of patient populations and healthcare systems. In this dissertation, I present the research conducted in the following areas: identifying patient groups, improving treatments for specific conditions by using statistical as well as data mining techniques, and developing new operation research models to increase system efficiency from the health institutes' perspective. The results provide better understanding of high risk patient groups, more accuracy in detecting disease' correlations and practical scheduling tools that consider uncertain operation durations and real-life constraints.
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