Machine learning methods for prediction of cardiovascular diseases
Abstract/Contents
- Abstract
- 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
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2021; ©2021 |
| Publication date | 2021; 2021 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Torres, Jessica Nicole |
|---|---|
| Degree supervisor | Bustamante, Carlos (Carlos D.) |
| Thesis advisor | Bustamante, Carlos (Carlos D.) |
| Thesis advisor | Wall, Dennis Paul |
| Thesis advisor | Zou, James |
| Degree committee member | Wall, Dennis Paul |
| Degree committee member | Zou, James |
| Associated with | Stanford University, School of Medicine, Department of Biomedical Data Science |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Jessica Torres |
|---|---|
| Note | Submitted to the Department of Biomedical Data Science |
| Thesis | Thesis Ph.D. Stanford University 2021 |
| Location | https://purl.stanford.edu/rq206js2148 |
Access conditions
- Copyright
- © 2021 by Jessica Nicole Torres
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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