東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Application of knowledge discovery ...

Taft, Laritza M.

FindBook

Google Book

Amazon

博客來

Application of knowledge discovery in databases methodologies for predictive models for pregnancy adverse events.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Application of knowledge discovery in databases methodologies for predictive models for pregnancy adverse events./
作者:	Taft, Laritza M.
面頁冊數:	111 p.
附註:	Source: Dissertation Abstracts International, Volume: 71-08, Section: B, page: 5092.
Contained By:	Dissertation Abstracts International71-08B.
標題:	Health Sciences, Obstetrics and Gynecology. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3412558
ISBN:	9781124113838

Application of knowledge discovery in databases methodologies for predictive models for pregnancy adverse events.
Taft, Laritza M.

Application of knowledge discovery in databases methodologies for predictive models for pregnancy adverse events. - 111 p.

Source: Dissertation Abstracts International, Volume: 71-08, Section: B, page: 5092.

Thesis (Ph.D.)--The University of Utah, 2010.

In its report To Err is Human, The Institute of Medicine recommended the implementation of internal and external voluntary and mandatory automatic reporting systems to increase detection of adverse events. Knowledge Discovery in Databases (KDD) allows the detection of patterns and trends that would be hidden or less detectable if analyzed by conventional methods.

ISBN: 9781124113838Subjects--Topical Terms:

1020690
Health Sciences, Obstetrics and Gynecology.

Application of knowledge discovery in databases methodologies for predictive models for pregnancy adverse events.
LDR:03126nam 2200373 4500 001 1402465
005 20111102140001.5
008 130515s2010 ||||||||||||||||| ||eng d
020 $a 9781124113838
035 $a (UMI)AAI3412558
035 $a AAI3412558
040 $a UMI $c UMI
100 1 $a Taft, Laritza M. $3 1681656
245 1 0 $a Application of knowledge discovery in databases methodologies for predictive models for pregnancy adverse events.
300 $a 111 p.
500 $a Source: Dissertation Abstracts International, Volume: 71-08, Section: B, page: 5092.
500 $a Adviser: R. Scott Evans.
502 $a Thesis (Ph.D.)--The University of Utah, 2010.
520 $a In its report To Err is Human, The Institute of Medicine recommended the implementation of internal and external voluntary and mandatory automatic reporting systems to increase detection of adverse events. Knowledge Discovery in Databases (KDD) allows the detection of patterns and trends that would be hidden or less detectable if analyzed by conventional methods.
520 $a The objective of this study was to examine novel KDD techniques used by other disciplines to create predictive models using healthcare data and validate the results through clinical domain expertise and performance measures.
520 $a Patient records for the present study were extracted from the enterprise data warehouse (EDW) from Intermountain Healthcare. Patients with reported adverse events were identified from ICD9 codes. A clinical classification of the ICD9 codes was developed, and the clinical categories were analyzed for risk factors for adverse events including adverse drug events. Pharmacy data were categorized and used for detection of drugs administered in temporal sequence with antidote drugs. Data sampling and data boosting algorithms were used as signal amplification techniques. Decision trees, Naive Bayes, Canonical Correlation Analysis, and Sequence Analysis were used as machine learning algorithms.
520 $a Performance measures of the classification algorithms demonstrated statistically significant improvement after the transformation of the dataset through KDD techniques, data boosting and sampling. Domain expertise was applied to validate clinical significance of the results.
520 $a KDD methodologies were applied successfully to a complex clinical dataset. The use of these methodologies was empirically proven effective in healthcare data through statistically significant measures and clinical validation. Although more research is required, we demonstrated the usefulness of KDD methodologies in knowledge extraction from complex clinical data.
590 $a School code: 0240.
650 4 $a Health Sciences, Obstetrics and Gynecology. $3 1020690
650 4 $a Information Technology. $3 1030799
690 $a 0380
690 $a 0489
710 2 $a The University of Utah. $b Biomedical Informatics. $3 1681657
773 0 $t Dissertation Abstracts International $g 71-08B.
790 1 0 $a Evans, R. Scott, $e advisor
790 1 0 $a Mitchell, Joyce A. $e committee member
790 1 0 $a Thornton, Sidney N. $e committee member
790 1 0 $a Bray, Bruce E. $e committee member
790 1 0 $a Varner, Michael W. $e committee member
790 $a 0240
791 $a Ph.D.
792 $a 2010
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3412558