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Text mining comorbidity codes in the...

Ferrell, Jennifer.

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Text mining comorbidity codes in the analysis of cardiopulmonary rehabilitation data.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Text mining comorbidity codes in the analysis of cardiopulmonary rehabilitation data./
作者:	Ferrell, Jennifer.
面頁冊數:	70 p.
附註:	Adviser: Caryn Thompson.
Contained By:	Masters Abstracts International45-01.
標題:	Biology, Biostatistics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1437390
ISBN:	9780542836299

Text mining comorbidity codes in the analysis of cardiopulmonary rehabilitation data.
Ferrell, Jennifer.

Text mining comorbidity codes in the analysis of cardiopulmonary rehabilitation data. - 70 p.

Adviser: Caryn Thompson.

Thesis (M.S.P.H.)--University of Louisville, 2006.

The purpose of this paper is to examine the process of text mining and using the results to show the possible benefits of cardiopulmonary rehabilitation. The 555 patients enrolled in the study were receiving inpatient cardiopulmonary rehabilitation. Each patient had comorbidity codes associated with them. These codes are secondary diagnoses to the cardiac or pulmonary event that resulted in their hospitalization. The patients had secondary conditions ranging in number from 1 to 10. The patients were assessed at admission and discharge for functional independence. Since there are numerous comorbidity codes for each patient, it would be difficult to analyze each one. Therefore, we can text mine these codes to create meaningful clusters to help in the analysis. This paper explains the process and theory of text mining and clustering. We use these results to perform statistical analysis to examine the benefits of cardiopulmonary rehabilitation.

ISBN: 9780542836299Subjects--Topical Terms:

1018416
Biology, Biostatistics.

Text mining comorbidity codes in the analysis of cardiopulmonary rehabilitation data.
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520 $a The purpose of this paper is to examine the process of text mining and using the results to show the possible benefits of cardiopulmonary rehabilitation. The 555 patients enrolled in the study were receiving inpatient cardiopulmonary rehabilitation. Each patient had comorbidity codes associated with them. These codes are secondary diagnoses to the cardiac or pulmonary event that resulted in their hospitalization. The patients had secondary conditions ranging in number from 1 to 10. The patients were assessed at admission and discharge for functional independence. Since there are numerous comorbidity codes for each patient, it would be difficult to analyze each one. Therefore, we can text mine these codes to create meaningful clusters to help in the analysis. This paper explains the process and theory of text mining and clustering. We use these results to perform statistical analysis to examine the benefits of cardiopulmonary rehabilitation.
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