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Semantic and Association Rule Mining...

Lee, Jong Youl.

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Semantic and Association Rule Mining-Based Knowledge Extension for Reusable Medical Equipment Lifecycle Management.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Semantic and Association Rule Mining-Based Knowledge Extension for Reusable Medical Equipment Lifecycle Management./
作者:	Lee, Jong Youl.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2017,
面頁冊數:	66 p.
附註:	Source: Masters Abstracts International, Volume: 79-09.
Contained By:	Masters Abstracts International79-09.
標題:	Information Technology. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10642654
ISBN:	9780355665062

Semantic and Association Rule Mining-Based Knowledge Extension for Reusable Medical Equipment Lifecycle Management.
Lee, Jong Youl.

Semantic and Association Rule Mining-Based Knowledge Extension for Reusable Medical Equipment Lifecycle Management. - Ann Arbor : ProQuest Dissertations & Theses, 2017 - 66 p.

Source: Masters Abstracts International, Volume: 79-09.

Thesis (M.S.)--Wayne State University, 2017.

This item must not be sold to any third party vendors.

For healthcare providers, using Reusable Medical Equipment (RME) has a strength in the cost-efficiency since it can be reused and reprocessed to multiple patients. Hence, estimating the maintenance (i.e., repair) cost during RME lifecycle has been a topic in healthcare domain. However, most of the existing research regarding RME has focused on the prediction without considering the domain knowledge of the cost in healthcare. This aim of the research is to propose the method of knowledge extension based on the post-mining (i.e., Association Rule Mining) interpreted by the domain knowledge (i.e., RME ontology and statistical cost domain knowledge) for RME lifecycle management. This contains finding the frequent rule patterns from the tremendous volumes of decision rules (i.e., Random Forest Rules) of the non-profit hospital's legacy database, which can make the pruned frameworks of each rule pattern linked and interpreted to the proper domain knowledge. The interpreted rule patterns make healthcare providers utilize them in the RME lifecycle management decision making.

ISBN: 9780355665062Subjects--Topical Terms:

1030799
Information Technology.
Subjects--Index Terms:

Association rule mining

Semantic and Association Rule Mining-Based Knowledge Extension for Reusable Medical Equipment Lifecycle Management.
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