Medical Doctor & Clinical Researcher
Applying machine learning to cardiovascular medicine to improve how we predict outcomes after myocardial infarction and heart failure.
About
Dr Liam Scanlon is a medical doctor currently working at Eastern Health and Victorian Heart Hospital, whose research sits at the intersection of cardiology and machine learning. His work focuses on integrating echocardiographic and clinical data into predictive models for long-term survival and readmission after myocardial infarction and heart failure, with the aim of giving clinicians better tools for risk stratification at the point of care. His research has been presented at CSANZ and published in the European Heart Journal, JACC: Advances, and the Journal of Interventional Cardiology.
Research
Scanlon L, Xiong E, Chan NI, Mallouhi M, Vollbon W, Atherton JJ, Lin A, Prasad SB.
European Heart Journal – Digital Health · 2026 Apr;7(3):ztag048
Scanlon L, Goel V, Lambrakis K, Seneviratne D, Lim A, Verjans J, Khan E, Stub D, Lin A, Chew D.
JACC: Advances · 2026
Scanlon L, Prasad SB, Krishnan A, Ivy Chan N, Mallouhi M, Vollbon W, Parsonage W, Khanna S, Lin A, Atherton JJ.
European Heart Journal Open · 2025 May;5(3):oeaf064
Goel V, Goel V, Scanlon L, O'Brien J, Vasanthakumar S, Paleri S, Stub D, Chew D, Nerlekar N, Brown AJ.
Journal of Interventional Cardiology · 2025;2025(1):2300133
In Progress
New-onset atrial fibrillation (NOAF) after myocardial infarction confers worse outcomes. This study develops and validates a machine learning survival model integrating clinical and echocardiographic data to predict NOAF post-MI. In a validation cohort of 1,344 patients, an XGBoost survival model outperformed Cox regression (C-index 0.860 vs 0.833) and a parsimonious 7-feature model offered comparable performance (C-index 0.849), providing a practical tool for targeting intensified rhythm surveillance.