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Discovery and Clinical Decision Supp...

Yoon, Jinsung.

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Discovery and Clinical Decision Support for Personalized Healthcare.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Discovery and Clinical Decision Support for Personalized Healthcare./
作者:	Yoon, Jinsung.
面頁冊數:	72 p.
附註:	Source: Masters Abstracts International, Volume: 55-04.
Contained By:	Masters Abstracts International55-04(E).
標題:	Electrical engineering. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10103778
ISBN:	9781339673226

Discovery and Clinical Decision Support for Personalized Healthcare.
Yoon, Jinsung.

Discovery and Clinical Decision Support for Personalized Healthcare. - 72 p.

Source: Masters Abstracts International, Volume: 55-04.

Thesis (M.S.)--University of California, Los Angeles, 2016.

With the advent of electronic health records, more data is continuously collected for individual patients and more data is available for review from past patients. Despite this, it has not yet been possible to successfully use this data to systematically build clinical decision support systems that can produce personalized clinical recommendations to assist clinicians in providing individualized healthcare. In this paper, we present a novel approach, Discovery Engine (DE) that discovers which patient characteristics are most relevant for predicting the correct diagnosis and/or recommending the best treatment regimen for each patient. We demonstrate the performance of DE in two clinical settings: diagnosis of breast cancer as well as personalized recommendation for a specific chemotherapy regimen for breast cancer patients. For each distinct clinical recommendation, different patient features are relevant; DE can discover these different relevant features and use them to recommend personalized clinical decisions. The DE approach achieves a 16.6% improvement over existing state-of-the-art recommendation algorithms in terms of kappa coefficients for recommending the personalized chemotherapy regimens. For diagnostic predictions, the DE approach achieves a 2.18% and 4.20% improvement over existing state-of-the-art prediction algorithms in terms of prediction error rate and false positive rate, respectively. We also demonstrate that the performance of our approach is robust against missing information, and that the relevant features discovered by DE are confirmed by clinical references.

ISBN: 9781339673226Subjects--Topical Terms:

649834
Electrical engineering.

Discovery and Clinical Decision Support for Personalized Healthcare.
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