자료유형  

 학위논문

Control Number  

0017163763

International Standard Book Number  

9798342108911

Dewey Decimal Classification Number  

616

Main Entry-Personal Name  

Hughes, John Weston.

Publication, Distribution, etc. (Imprint  

[S.l.] : Stanford University., 2024

Publication, Distribution, etc. (Imprint  

Ann Arbor : ProQuest Dissertations & Theses, 2024

Physical Description  

152 p.

General Note  

Source: Dissertations Abstracts International, Volume: 86-04, Section: B.

General Note  

Advisor: Guestrin, Carlos;Zou, James;Kundaje, Anshul.

Dissertation Note  

Thesis (Ph.D.)--Stanford University, 2024.

Summary, Etc.  

요약Heart disease is the number one cause of mortality in the US and globally even as preventative and interventional care have improved significantly in the past decades, in part because treatment isn't getting to the right people at the right time. Hospitals collect huge amounts of cardiovascular data, in the form of imaging, biosignals, text, and structured data. Could artificial intelligence (AI) help use this data to improve outcomes? This thesis builds on work applying AI to echocardiogram and electrocardiogram data with the goal of improving the set of tasks AI used for, how models are evaluated and interpreted, and our understanding of what diagnostic modalities are capable of. We start by describing a new computer vision tool for detecting abnormal laboratory values from the echocardiogram. Next, we present a novel AI risk score for cardiovascular mortality and disease from the ECG, which in combination with current standard-of-care cardiovascular risk scores can improve risk stratification for decisions like statin prescription. Third, we demonstrate via case study a new way of thinking about deep learning for electrocardiography by exploring a range of risk scores for left ventricular systolic dysfunction (LVSD) ranging from single measurements taken from the ECG to deep learning models with hundreds of thousands of parameters. Finally, we present a novel deep learning phenome-wide association study, analyzing the connection between the ECG and over 500 different diseases.

Subject Added Entry-Topical Term  

Cardiovascular disease.

Subject Added Entry-Topical Term  

Biomarkers.

Subject Added Entry-Topical Term  

Electrocardiography.

Subject Added Entry-Topical Term  

Medicine.

Subject Added Entry-Topical Term  

Public health.

Added Entry-Corporate Name  

Stanford University.

Host Item Entry  

Dissertations Abstracts International. 86-04B.

Electronic Location and Access  

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Control Number  

joongbu:658338