Artificial Intelligence in Modelling the Influence of Socio-Economic Factors on the Risk of Cardiovascular Events

Studentship strategic priority area: Mathematics, Statistics and Computation
Keywords: Cardiovascular Disease, Artificial Intelligence

Cardiovascular diseases (CVDs) are leading cause of morbidity and mortality worldwide, especially in older people, despite substantial advances in diagnosis and treatment. Application of artificial intelligence (AI) offers state-of-the-art data modelling and interpretation to inform and support clinical decisions. AI techniques such as machine learning and deep learning can improve medical knowledge, by providing advanced insights into healthcare choices based on longitudinal health records. The emerging multidisciplinary field of healthcare and AI will become increasingly important in informing people and healthcare professionals about how medical events and decisions are associated with and potential influence outcome. However, medical events may be strongly influenced by affluence and social deprivation.

My research will use NHS administrative data from Greater Glasgow & Clyde Health Board for adults age >50 years (the age at which the incidence and prevalence of disease affecting older people increases rapidly). This comprises a large dataset including demographics, blood tests, electrocardiograms and echocardiograms, primary-care prescriptions, hospitalisations and procedures and mortality. For a large subset, additional primary care data on smoking, blood pressure and body mass index can be obtained. I will use these data to explore the common sequence of events (e.g. smoking, hypertension and obesity, leading to diabetes, renal dysfunction and atherosclerosis and onwards to myocardial infarction, heart failure, stroke, disability and death) and accounting for age, their relationship to affluence and social deprivation.