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Modeling of hemodialysis patient hem...

Bries, Michael Francis.

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Modeling of hemodialysis patient hemoglobin: A data mining exploration.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Modeling of hemodialysis patient hemoglobin: A data mining exploration./
作者:	Bries, Michael Francis.
面頁冊數:	107 p.
附註:	Source: Masters Abstracts International, Volume: 45-05, page: 2337.
Contained By:	Masters Abstracts International45-05.
標題:	Biology, Bioinformatics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1442752

Modeling of hemodialysis patient hemoglobin: A data mining exploration.
Bries, Michael Francis.

Modeling of hemodialysis patient hemoglobin: A data mining exploration. - 107 p.

Source: Masters Abstracts International, Volume: 45-05, page: 2337.

Thesis (M.S.)--The University of Iowa, 2007.

Data mining is emerging as an important tool in many areas of research and industry. Companies and organizations are increasingly interested in applying data mining tools to increase the value added by their data collections systems. Nowhere is this potential more important than in the healthcare industry. As medical records systems become more standardized and commonplace, data quantity increases with much of it going unanalyzed.Subjects--Topical Terms:

1018415
Biology, Bioinformatics.

Modeling of hemodialysis patient hemoglobin: A data mining exploration.
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502 $a Thesis (M.S.)--The University of Iowa, 2007.
520 $a Data mining is emerging as an important tool in many areas of research and industry. Companies and organizations are increasingly interested in applying data mining tools to increase the value added by their data collections systems. Nowhere is this potential more important than in the healthcare industry. As medical records systems become more standardized and commonplace, data quantity increases with much of it going unanalyzed.
520 $a Data mining can begin to leverage some of this data into tools that help clinicians organize data and make decisions. These modeling techniques are explored in the following text. Through the use of clustering and classification techniques, accurate models of a dialysis patient's current status are derived. The K-Means and Expectation Maximization clustering algorithms are utilized to generate homogeneous patient populations. Classification techniques, such as decision trees, neural networks, and the Naive Bayes classifier are evaluated in terms of their accuracy performance. Time series aspects are also considered utilizing system identification techniques from control theory. Finally, cluster-derived classification models are tested for their cross-validation accuracy, as well as the generalizability to unseen testing sets.
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