國立虎尾科技大學 |

Collaborative data mining for clinical trial analytics.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Collaborative data mining for clinical trial analytics./
作者:	Gholap, Jay.
面頁冊數:	1 online resource (79 pages)
附註:	Source: Masters Abstracts International, Volume: 55-05.
Contained By:	Masters Abstracts International55-05(E).
標題:	Information science. -
電子資源:	click for full text (PQDT)
ISBN:	9781339959689

Collaborative data mining for clinical trial analytics.
Gholap, Jay.

Collaborative data mining for clinical trial analytics. - 1 online resource (79 pages)

Source: Masters Abstracts International, Volume: 55-05.

Thesis (M.S.)

Includes bibliographical references

Clinical trials for clinical research and drug development generate a large amount of data. Due to dispersed nature of clinical trial data, it remains a challenge to harness this data for analytics. In this thesis, we propose a novel method of collaborative data mining to provide multi-level analysis of clinical trial data consolidated from multiple datasets with the help of master data management (MDM) techniques. Our aim is to validate findings by collaborative utilization of various data mining techniques such as classification, clustering and association rule mining. We complement our results with the help of interactive visualizations. The thesis also demonstrates a use of data stratification for identifying disparities between various subgroups of clinical trial participants. Overall, our approach aims at extracting useful knowledge from clinical trial data in order to improve design of clinical trials. We provide promising experimental results with drug abuse and Osteoarthritis clinical trial data.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9781339959689Subjects--Topical Terms:

561178
Information science.
Index Terms--Genre/Form:

554714
Electronic books.

Collaborative data mining for clinical trial analytics.
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504 $a Includes bibliographical references
520 $a Clinical trials for clinical research and drug development generate a large amount of data. Due to dispersed nature of clinical trial data, it remains a challenge to harness this data for analytics. In this thesis, we propose a novel method of collaborative data mining to provide multi-level analysis of clinical trial data consolidated from multiple datasets with the help of master data management (MDM) techniques. Our aim is to validate findings by collaborative utilization of various data mining techniques such as classification, clustering and association rule mining. We complement our results with the help of interactive visualizations. The thesis also demonstrates a use of data stratification for identifying disparities between various subgroups of clinical trial participants. Overall, our approach aims at extracting useful knowledge from clinical trial data in order to improve design of clinical trials. We provide promising experimental results with drug abuse and Osteoarthritis clinical trial data.
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538 $a Mode of access: World Wide Web
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