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Doctor Narinder Kaur

University of Glasgow, Glasgow (United Kingdom of Great Britain & Northern Ireland)

Member of:

European Society of Cardiology

Narinder is a passionate and innovative Precision Medicine Scientist and PhD Candidate who is putting in substantial effort to build a strong multidisciplinary field of Cardiovascular Disease (CVD) and Artificial Intelligence. Her goal is to investigate the definitive causes of CVD and apply state-of-the-art AI methods to identify distinct patient lifestyle profiles concerning multimorbidity. She is currently implementing survival-based models to uncover prognostic factors and pathways to cardiovascular disease progression in patients with type 2 diabetes mellitus. This is carried out by the efficient usage of electronic health records from NHS Scotland Greater Glasgow & Clyde and Hong Kong. Overall, the research introduces new approaches to support healthcare professionals manage patients with cardiovascular risk events.

Incident use of loop diuretics and incident heart failure in type-2 diabetes mellitus: a precision medicine approach

Event: ESC Congress 2025

Topic: Artificial Intelligence

Session: Integrating electronic health record data with digital tools: a paradigm shift in cardiology

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The inclusion of cancer in heart failure risk assessment tool for patients with type-2 diabetes mellitus: a precision medicine approach

Event: ESC Congress 2025

Topic: Precision Medicine

Session: Precision diagnostics to guide precision therapy

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Predicting mortality of type-2 diabetes mellitus by applying machine learning to electronic medical records in the west of scotland and hong kong

Event: ESC Congress 2024

Topic: Artificial Intelligence

Session: Progress and challenges in artificial intelligence for cardiovascular risk assessment and diagnosis

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Novel support tool for predicting incident heart failure in type-2 diabetes mellitus: a precision medicine approach

Event: Heart Failure 2024

Topic: m-Health & Mobile Apps

Session: e-Cardiology/digital health

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Use of machine learning to predict mortality in patients with type 2 diabetes mellitus, according to socioeconomic status

Event: ESC Congress 2023

Topic: Artificial Intelligence

Session: Harnessing the power of artificial intelligence in the clinic

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Predicting incident heart failure in patients with type ii diabetes: a machine a learning approach

Event: Heart Failure 2023

Topic: m-Health & Mobile Apps

Session: ePosters in e-cardiology/digital health 2

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