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Machine Learning Methods for Prediction of Cardiovascular Diseases.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Machine Learning Methods for Prediction of Cardiovascular Diseases./
Author:	Torres, Jessica.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
Description:	157 p.
Notes:	Source: Dissertations Abstracts International, Volume: 83-05, Section: B.
Contained By:	Dissertations Abstracts International83-05B.
Subject:	Artificial intelligence. -
Online resource:	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.
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502 $a Thesis (Ph.D.)--Stanford University, 2021.
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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.
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