Big data in heart failure - opportunities and realities

Despite advances in treatment, our ability to tailor strategies for prevention or management to individuals with heart failure is currently limited. Large-scale electronic health records and novel data analysis techniques have great potential to improve the status quo in both research and practice. In this talk, Amitava Banerjee examines the real progress and the limitations of recent big data research in heart failure, from epidemiology to machine learning.

Amitava Banerjee is Associate Professor in Clinical Data Science at University College London, and Honorary Consultant Cardiologist at University College London Hospitals and Barts Health NHS Trusts. He is a pragmatic researcher, a passionate educator and a practising clinician, with interests spanning data science, cardiovascular disease, global health, training and evidence-based healthcare.

After qualifying from Oxford Medical School, he trained as a junior doctor in Oxford, Newcastle, Hull and London. His interest in preventive cardiology and evidence-based medicine led to a Masters in Public Health at Harvard (2004/05), an internship at the World Health Organisation(2005) and DPhil in epidemiology from Oxford (2010). He was Clinical Lecturer in Cardiovascular Medicine at the University of Birmingham, before moving to UCL in 2015.

He works across two busy tertiary care settings: University College London Hospitals and Barts Health NHS Trusts with both inpatient and outpatient commitments. Although he is subspecialised in heart failure, he has ongoing practice in acute general cardiology and a keen interest in the diagnosis and management of atrial fibrillation. His clinical work very much informs his research and vice versa, whether in the evaluation of medical technology or the ethics of large-scale use of patient data.

This talk was held as part of the Big Data Epidemiology module which is part of the MSc in Evidence-Based Health Care and the MSc in EBHC Medical Statistics.