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

Approaches to Extract Deep Phenotype...

Luong, Duc Thanh Anh.

FindBook

Google Book

Amazon

博客來

Approaches to Extract Deep Phenotypes from Clinical Data.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Approaches to Extract Deep Phenotypes from Clinical Data./
作者:	Luong, Duc Thanh Anh.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2019,
面頁冊數:	120 p.
附註:	Source: Dissertations Abstracts International, Volume: 80-09, Section: B.
Contained By:	Dissertations Abstracts International80-09B.
標題:	Computer Engineering. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10975526
ISBN:	9780438944442

Approaches to Extract Deep Phenotypes from Clinical Data.
Luong, Duc Thanh Anh.

Approaches to Extract Deep Phenotypes from Clinical Data. - Ann Arbor : ProQuest Dissertations & Theses, 2019 - 120 p.

Source: Dissertations Abstracts International, Volume: 80-09, Section: B.

Thesis (Ph.D.)--State University of New York at Buffalo, 2019.

This item must not be sold to any third party vendors.

Computational phenotyping is an emerging topic in health informatics. An important catalyst of this emergence is the increasing volume of clinical data available for analysis. However, clinical data typically consists of many disparate elements and has strong temporal dependencies, both of which require designing new machine learning algorithms that can extract useful knowledge from such data. In this dissertation, my focus is on developing techniques to extract longitudinal phenotypes from clinical data. In particular, we have examined three different approaches to obtain longitudinal disease subtypes for Chronic Kidney Disease (CKD) - a rising health problem in both US and worldwide. First, a probabilistic model is applied to empirically obtained phenotypes by computing the disease subtyping effect while "explaining away" other factors such as subtyping effect, long-term and short-term effects from clinical observations.Second, a temporal k-means algorithm was proposed to group patients with similar disease progressions into clusters. Third, we perform an in-depth analysis of Time-Aware Long Short-Term Memory Autoencoder, a deep learning approach, to project longitudinal patient profiles into a common latent space and subsequently use latent representations to identify unusual disease progressions. In order to evaluate the quality of disease subtypes obtained from these approaches, we also propose a quantitative evaluation metric by estimating the tightness of the resulting clusters as well as the degree of separation between different clusters.

ISBN: 9780438944442Subjects--Topical Terms:

1567821
Computer Engineering.

Approaches to Extract Deep Phenotypes from Clinical Data.
LDR:02661nmm a2200337 4500 001 2208114
005 20190929184218.5
008 201008s2019 ||||||||||||||||| ||eng d
020 $a 9780438944442
035 $a (MiAaPQ)AAI10975526
035 $a (MiAaPQ)buffalo:16138
035 $a AAI10975526
040 $a MiAaPQ $c MiAaPQ
100 1 $a Luong, Duc Thanh Anh. $3 3435126
245 1 0 $a Approaches to Extract Deep Phenotypes from Clinical Data.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2019
300 $a 120 p.
500 $a Source: Dissertations Abstracts International, Volume: 80-09, Section: B.
500 $a Publisher info.: Dissertation/Thesis.
500 $a Advisor: Chandola, Varun.
502 $a Thesis (Ph.D.)--State University of New York at Buffalo, 2019.
506 $a This item must not be sold to any third party vendors.
520 $a Computational phenotyping is an emerging topic in health informatics. An important catalyst of this emergence is the increasing volume of clinical data available for analysis. However, clinical data typically consists of many disparate elements and has strong temporal dependencies, both of which require designing new machine learning algorithms that can extract useful knowledge from such data. In this dissertation, my focus is on developing techniques to extract longitudinal phenotypes from clinical data. In particular, we have examined three different approaches to obtain longitudinal disease subtypes for Chronic Kidney Disease (CKD) - a rising health problem in both US and worldwide. First, a probabilistic model is applied to empirically obtained phenotypes by computing the disease subtyping effect while "explaining away" other factors such as subtyping effect, long-term and short-term effects from clinical observations.Second, a temporal k-means algorithm was proposed to group patients with similar disease progressions into clusters. Third, we perform an in-depth analysis of Time-Aware Long Short-Term Memory Autoencoder, a deep learning approach, to project longitudinal patient profiles into a common latent space and subsequently use latent representations to identify unusual disease progressions. In order to evaluate the quality of disease subtypes obtained from these approaches, we also propose a quantitative evaluation metric by estimating the tightness of the resulting clusters as well as the degree of separation between different clusters.
590 $a School code: 0656.
650 4 $a Computer Engineering. $3 1567821
650 4 $a Biomedical engineering. $3 535387
650 4 $a Computer science. $3 523869
690 $a 0464
690 $a 0541
690 $a 0984
710 2 $a State University of New York at Buffalo. $b Computer Science and Engineering. $3 1035503
773 0 $t Dissertations Abstracts International $g 80-09B.
790 $a 0656
791 $a Ph.D.
792 $a 2019
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10975526