Artificial intelligence methods to support clinical decision making in cardiology

Juan Lu

doi:10.26182/499h-wm85

Artificial intelligence methods to support clinical decision making in cardiology

Juan Lu

Research output: Thesis › Doctoral Thesis

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Abstract

This thesis explores the transformative impact of artificial intelligence (AI) in cardiology, focusing on coronary artery disease (CAD) and atrial fibrillation (AF). It examines AI's potential to enhance cardiovascular diagnostic, prognostic, and treatment decision-making processes. Key innovations include deep learning algorithms for predicting exercise stress test outcomes in CAD, a multi-modal model combining CCTA volume data with demographics to predict major adverse cardiovascular events, and multi-label ML models predicting risks for AF patients. Additionally, it evaluates ML models for predicting acute coronary syndrome and mortality risks from COX-2 inhibitors. The findings aim to improve clinical outcomes and streamline healthcare operations.

Original language	English
Qualification	Doctor of Philosophy
Awarding Institution	The University of Western Australia
Supervisors/Advisors	Dwivedi, Girish, Supervisor Bennamoun, Mohammed, Supervisor Murray, Kevin, Supervisor Sanfilippo, Frank, Supervisor Chow, Benjamin J.W., Supervisor, External person
Thesis sponsors	Australian Government Research Training Program
Award date	13 Nov 2024
DOIs	https://doi.org/10.26182/499h-wm85
Publication status	Unpublished - 2024

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10.26182/499h-wm85

THESIS - DOCTOR OF PHILOSOPHY - LU Juan - 2024
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5 Article

Predicting multifaceted risks using machine learning in atrial fibrillation: insights from GLORIA-AF study
Lu, J., Bisson, A., Bennamoun, M., Zheng, Y., Sanfilippo, F. M., Hung, J., Briffa, T., McQuillan, B., Stewart, J., Figtree, G., Huisman, M. V., Dwivedi, G. & Lip, G. Y. H., May 2024, In: European Heart Journal - Digital Health. 5, 3, p. 235-246 12 p.
Research output: Contribution to journal › Article › peer-review

Open Access
3 Citations (Scopus)
Deep learning model to predict exercise stress test results: Optimizing the diagnostic test selection strategy and reduce wastage in suspected coronary artery disease patients
Lu, J., Stewart, J., Bennamoun, M., Goudie, A., Eshraghian, J., Ihdayhid, A., Sanfilippo, F., Small, G. R., Chow, B. J. & Dwivedi, G., Oct 2023, In: Computer Methods and Programs in Biomedicine. 240, 107717.
Research output: Contribution to journal › Article › peer-review

Open Access
6 Citations (Scopus)
Western Australian medical students' attitudes towards artificial intelligence in healthcare
Stewart, J., Lu, J., Gahungu, N., Goudie, A., Fegan, P. G., Bennamoun, M., Sprivulis, P. & Dwivedi, G., Aug 2023, In: PLoS One. 18, 8 August, e0290642.
Research output: Contribution to journal › Article › peer-review

Open Access
21 Citations (Web of Science)

Cite this

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title = "Artificial intelligence methods to support clinical decision making in cardiology",

abstract = "This thesis explores the transformative impact of artificial intelligence (AI) in cardiology, focusing on coronary artery disease (CAD) and atrial fibrillation (AF). It examines AI's potential to enhance cardiovascular diagnostic, prognostic, and treatment decision-making processes. Key innovations include deep learning algorithms for predicting exercise stress test outcomes in CAD, a multi-modal model combining CCTA volume data with demographics to predict major adverse cardiovascular events, and multi-label ML models predicting risks for AF patients. Additionally, it evaluates ML models for predicting acute coronary syndrome and mortality risks from COX-2 inhibitors. The findings aim to improve clinical outcomes and streamline healthcare operations.",

keywords = "machine learning, cardiology, coronary artery disease",

author = "Juan Lu",

year = "2024",

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school = "The University of Western Australia",

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AB - This thesis explores the transformative impact of artificial intelligence (AI) in cardiology, focusing on coronary artery disease (CAD) and atrial fibrillation (AF). It examines AI's potential to enhance cardiovascular diagnostic, prognostic, and treatment decision-making processes. Key innovations include deep learning algorithms for predicting exercise stress test outcomes in CAD, a multi-modal model combining CCTA volume data with demographics to predict major adverse cardiovascular events, and multi-label ML models predicting risks for AF patients. Additionally, it evaluates ML models for predicting acute coronary syndrome and mortality risks from COX-2 inhibitors. The findings aim to improve clinical outcomes and streamline healthcare operations.

KW - machine learning

KW - cardiology

KW - coronary artery disease

U2 - 10.26182/499h-wm85

DO - 10.26182/499h-wm85

M3 - Doctoral Thesis

ER -

Artificial intelligence methods to support clinical decision making in cardiology

Abstract

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Research output

Predicting multifaceted risks using machine learning in atrial fibrillation: insights from GLORIA-AF study

Deep learning model to predict exercise stress test results: Optimizing the diagnostic test selection strategy and reduce wastage in suspected coronary artery disease patients

Western Australian medical students' attitudes towards artificial intelligence in healthcare

Cite this