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

FindBook

Google Book

Amazon

博客來

Machine Learning Methods for Prediction of Cardiovascular Diseases.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Machine Learning Methods for Prediction of Cardiovascular Diseases./
作者:	Torres, Jessica.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:	157 p.
附註:	Source: Dissertations Abstracts International, Volume: 83-05, Section: B.
Contained By:	Dissertations Abstracts International83-05B.
標題:	Artificial intelligence. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28812867
ISBN:	9798494453167

Machine Learning Methods for Prediction of Cardiovascular Diseases.
Torres, Jessica.

Machine Learning Methods for Prediction of Cardiovascular Diseases. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 157 p.

Source: Dissertations Abstracts International, Volume: 83-05, Section: B.

Thesis (Ph.D.)--Stanford University, 2021.

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

Artificial intelligence and machine learning methods in cardiology are increasingly used in a wide array of remote monitoring applications and within clinical workflows. The acceptance of such methods is due to the demonstrated benefits of deep learning algorithms to achieve state-of-the-art results and surpass human accuracy in challenging clinical tasks. Concurrently, the advancements in wearable technology have allowed for an unprecedented look into human health and activity. This snapshot into the objective measurements of human health can enable clinicians, researchers, and patients to measure and track a new dimension of human health at a granularity not previously possible. Herein, I present work that focuses on methods to advance remote monitoring and prediction models for cardiovascular diseases. First, I introduce a multi-task deep learning method to jointly assess signal quality and arrhythmia event detection in wearable photoplethysmography devices for real-time detection of atrial fibrillation. Second, I describe a multimodal neural network model to predict the etiology of left ventricular hypertrophy from electrocardiograms and echocardiograms. Together, these results demonstrate new applications of machine learning tools that may assist in the study of cardiovascular disease progression inside and outside a clinical setting.

ISBN: 9798494453167Subjects--Topical Terms:

516317
Artificial intelligence.

Machine Learning Methods for Prediction of Cardiovascular Diseases.
LDR:02389nmm a2200301 4500 001 2349869
005 20221010063643.5
008 241004s2021 ||||||||||||||||| ||eng d
020 $a 9798494453167
035 $a (MiAaPQ)AAI28812867
035 $a (MiAaPQ)STANFORDrq206js2148
035 $a AAI28812867
040 $a MiAaPQ $c MiAaPQ
100 1 $a Torres, Jessica. $3 3689292
245 1 0 $a Machine Learning Methods for Prediction of Cardiovascular Diseases.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2021
300 $a 157 p.
500 $a Source: Dissertations Abstracts International, Volume: 83-05, Section: B.
500 $a Advisor: Bustamante, Carlos;Wall, Dennis;Zou, James;Ashley, Euan.
502 $a Thesis (Ph.D.)--Stanford University, 2021.
506 $a This item must not be sold to any third party vendors.
520 $a Artificial intelligence and machine learning methods in cardiology are increasingly used in a wide array of remote monitoring applications and within clinical workflows. The acceptance of such methods is due to the demonstrated benefits of deep learning algorithms to achieve state-of-the-art results and surpass human accuracy in challenging clinical tasks. Concurrently, the advancements in wearable technology have allowed for an unprecedented look into human health and activity. This snapshot into the objective measurements of human health can enable clinicians, researchers, and patients to measure and track a new dimension of human health at a granularity not previously possible. Herein, I present work that focuses on methods to advance remote monitoring and prediction models for cardiovascular diseases. First, I introduce a multi-task deep learning method to jointly assess signal quality and arrhythmia event detection in wearable photoplethysmography devices for real-time detection of atrial fibrillation. Second, I describe a multimodal neural network model to predict the etiology of left ventricular hypertrophy from electrocardiograms and echocardiograms. Together, these results demonstrate new applications of machine learning tools that may assist in the study of cardiovascular disease progression inside and outside a clinical setting.
590 $a School code: 0212.
650 4 $a Artificial intelligence. $3 516317
650 4 $a Neural networks. $3 677449
650 4 $a Support vector machines. $3 2058743
690 $a 0800
710 2 $a Stanford University. $3 754827
773 0 $t Dissertations Abstracts International $g 83-05B.
790 $a 0212
791 $a Ph.D.
792 $a 2021
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28812867