Overview
Background
Dr Cecil Mustafiz is a junior doctor working in Queensland Health with a strong clinical and academic interest in cardiology. He holds Associate Lecturer appointments at both The University of Queensland and Griffith University. He shares a passion for teaching and supervising medical students, as well as conducting clinical and translational research in the field of cardiovascular medicine. His work has resulted in peer-reviewed publications in recognised cardiovascular medicine journals, and he has contributed to abstracts and presentations at national and international cardiology conferences. Dr Mustafiz also contributes to the academic community as an invited peer reviewer for BMC Cardiovascular Disorders and Scientific Reports (Nature Portfolio).
Availability
- Dr Cecil Mustafiz is:
- Not available for supervision
Fields of research
Qualifications
- Masters (Extended) of Medicine, Griffith University
- Member, Cardiac Society of Australia and New Zealand, Cardiac Society of Australia and New Zealand
Research interests
-
Cardiology
-
Valvular Heart Disease
-
Coronary Artery Disease
-
Heart Failure
-
Artificial Intelligence
Works
Search Professor Cecil Mustafiz’s works on UQ eSpace
2025
Journal Article
Machine-learning versus traditional approaches to predict all-cause mortality for acute coronary syndrome: a systematic review and meta-analysis
Gupta, Aashray K., Mustafiz, Cecil, Mutahar, Daud, Zaka, Ammar, Parvez, Razeen, Mridha, Naim, Stretton, Brandon, Kovoor, Joshua G., Bacchi, Stephen, Ramponi, Fabio, Chan, Justin C.Y., Zaman, Sarah, Chow, Clara, Kovoor, Pramesh, Bennetts, Jayme S. and Maddern, Guy J. (2025). Machine-learning versus traditional approaches to predict all-cause mortality for acute coronary syndrome: a systematic review and meta-analysis. Canadian Journal of Cardiology, 41 (8), 1564-1583. doi: 10.1016/j.cjca.2025.01.037
2025
Journal Article
Machine-learning versus traditional methods for prediction of all-cause mortality after transcatheter aortic valve implantation: a systematic review and meta-analysis
Zaka, Ammar, Mustafiz, Cecil, Mutahar, Daud, Sinhal, Shreyans, Gorcilov, James, Muston, Benjamin, Evans, Shaun, Gupta, Aashray, Stretton, Brandon, Kovoor, Joshua, Mridha, Naim, Sivagangabalan, Gopal, Thiagalingam, Aravinda, Ramponi, Fabio, Chan, Justin, Bennetts, Jayme, Murdoch, Dale J., Zaman, Sarah, Chow, Clara K., Jayasinghe, Rohan, Kovoor, Pramesh and Bacchi, Stephen (2025). Machine-learning versus traditional methods for prediction of all-cause mortality after transcatheter aortic valve implantation: a systematic review and meta-analysis. Open Heart, 12 (1) e002779, 1-13. doi: 10.1136/openhrt-2024-002779
2024
Conference Publication
Artificial intelligence for the prediction of all-cause mortality and readmission in heart failure: a meta-analysis of twenty studies
Gupta, Aashray, Zaka, Ammar, Mutahar, Daud, Mustafiz, Cecil, Gorcilov, James, Abtahi, Johayer, Kamalanathan, Harish, Tan, Sheryn, Hains, Lewis, Sharma, Prakriti, Sharma, Srishti, Ragunath, Priyyanca, Kovoor, Joshua, Stretton, Brandon, Mridha, Naim and Bacchi, Stephen (2024). Artificial intelligence for the prediction of all-cause mortality and readmission in heart failure: a meta-analysis of twenty studies. American Heart Association's 2024 Scientific Sessions and the American Heart Association's 2024 Resuscitation Science Symposium, Chicago, IL United States, 16–18 November 2024. Philadelphia, PA United States: Lippincott Williams & Wilkins. doi: 10.1161/circ.150.suppl_1.4146070
2024
Conference Publication
Machine Learning for Prediction of All-Cause Mortality in Acute Coronary Syndrome
Gupta, Aashray, Zaka, Ammar, Mustafiz, Cecil, Mutahar, Daud, Parvez, Razeen, Stretton, Brandon, Kovoor, Joshua, Mridha, Naim, Ramponi, Fabio, Chan, Justin, Gould, Paul, Sivagangabalan, Gopal, Zaman, Sarah, Thiagalingam, Aravinda, Chow, Clara, Kovoor, Pramesh, Bacchi, Stephen, Bennetts, Jayme and Maddern, Guy (2024). Machine Learning for Prediction of All-Cause Mortality in Acute Coronary Syndrome. American Heart Association's 2024 Scientific Sessions and the American Heart Association's 2024 Resuscitation Science Symposium, Chicago, IL United States, 16–18 November 2024. Philadelphia, PA United States: Lippincott Williams & Wilkins. doi: 10.1161/circ.150.suppl_1.4138355
2024
Conference Publication
Machine learning for prediction of all-cause mortality in acute coronary syndrome
Zaka, A., Gupta, A., Mustafiz, C., Mutahar, D., Parvez, R., Stretton, B., Kovoor, J., Mridha, N. and Bacchi, S. (2024). Machine learning for prediction of all-cause mortality in acute coronary syndrome. ESC Congress 2024, London, United Kingdom, 30 August – 2 September 2024. Oxford, United Kingdom: Oxford University Press. doi: 10.1093/eurheartj/ehae666.3448
2024
Conference Publication
Machine Learning for Prediction of All-Cause Mortality in Acute Coronary Syndrome
Zaka, A., Gupta, A.K., Mustafiz, C., Mutahar, D., Parvez, R., Stretton, B., Kovoor, J.G., Mridha, N. and Bacchi, S. (2024). Machine Learning for Prediction of All-Cause Mortality in Acute Coronary Syndrome. 72nd Annual Scientific Meeting of the Cardiac Society of Australia and New Zealand, Perth, WA Australia, 1-4 August 2024. Chatswood, NSW Australia: Elsevier. doi: 10.1016/j.hlc.2024.06.566
2024
Conference Publication
Improving Risk Prediction After Transcatheter Aortic Valve Implantation: A Comparison of Machine Learning With Traditional Methods
Zaka, A., Mutahar, D., Mustafiz, C., Sinhal, S., Gorcilov, J., Evans, S., Gupta, A., Stretton, B., Kovoor, J., Mridha, N., Sivagangabalan, G., Zaman, S., Chow, C., Thiagalingam, A., Kovoor, P. and Bacchi, S. (2024). Improving Risk Prediction After Transcatheter Aortic Valve Implantation: A Comparison of Machine Learning With Traditional Methods. 72nd Annual Scientific Meeting of the Cardiac Society of Australia and New Zealand, Perth, WA Australia, 1-4 August 2024. Chatswood, NSW Australia: Elsevier. doi: 10.1016/j.hlc.2024.06.561
2024
Conference Publication
Artificial Intelligence for the Prediction of All-Cause Mortality and Readmission in Heart Failure: A Meta-Analysis of 558,233 Patients
Zaka, A., Mutahar, D., Mustafiz, C., Gorcilov, J., Abtahi, J., Kamalanathan, H., Tan, S., Hains, L., Sharma, P., Sharma, S., Ragunath, P., Gupta, A., Stretton, B., Kovoor, J., Mridha, N. and Bacchi, S. (2024). Artificial Intelligence for the Prediction of All-Cause Mortality and Readmission in Heart Failure: A Meta-Analysis of 558,233 Patients. 72nd Annual Scientific Meeting of the Cardiac Society of Australia and New Zealand, Perth, WA Australia, 1-4 August 2024. Chatswood, NSW Australia: Elsevier. doi: 10.1016/j.hlc.2024.06.545
Media
Enquiries
For media enquiries about Dr Cecil Mustafiz's areas of expertise, story ideas and help finding experts, contact our Media team: