BHF Data Science Centre

The impact of the COVID-19 pandemic on cardiovascular disease prevention and management

section { background: white; color: black; border-radius: 1em; padding: 1em; left: 50% } #inner { display: inline-block; display: flex; align-items: center; justify-content: center } Twitter thread summarising key results

Using national electronic health records for pandemic preparedness: validation of a parsimonious model for predicting excess deaths among those with COVID-19–a data-driven retrospective cohort study

section { background: white; color: black; border-radius: 1em; padding: 1em; left: 50% } #inner { display: inline-block; display: flex; align-items: center; justify-content: center } Twitter thread by Prof Ami Banerjee summarising key results

Digital ethnicity data in population-wide electronic health records in England: a description of completeness, coverage, and granularity of diversity

section { background: white; color: black; border-radius: 1em; padding: 1em; left: 50% } #inner { display: inline-block; display: flex; align-items: center; justify-content: center } 📺 BHF Data Science Centre seminar by Dr Sara Khalid

Association of COVID-19 With Major Arterial and Venous Thrombotic Diseases: A Population-Wide Cohort Study of 48 Million Adults in England and Wales

section { background: white; color: black; border-radius: 1em; padding: 1em; left: 50% } #inner { display: inline-block; display: flex; align-items: center; justify-content: center } 📺 BHF Data Science Centre seminar by Dr Samantha Ip & Dr Hoda Abbasizanjani

COVID-19 trajectories among 57 million adults in England: a cohort study using electronic health records

section { background: white; color: black; border-radius: 1em; padding: 1em; left: 50% } #inner { display: inline-block; display: flex; align-items: center; justify-content: center } Twitter thread summarising key results

BHF Data Science Centre Research Showcase: Phenotyping COVID-19: Insights from linked data for 56 million individuals

Evaluation of antithrombotic use and COVID-19 outcomes in a nationwide atrial fibrillation cohort

section { background: white; color: black; border-radius: 1em; padding: 1em; left: 50% } #inner { display: inline-block; display: flex; align-items: center; justify-content: center } Twitter thread

Using national electronic health records for pandemic preparedness: validation of a parsimonious model for predicting excess deaths among those with COVID-19

section { background: white; color: black; border-radius: 1em; padding: 1em; left: 50% } #inner { display: inline-block; display: flex; align-items: center; justify-content: center } ❗ Now published in Journal of the Royal Society of Medicine

The adverse impact of COVID-19 pandemic on cardiovascular disease prevention and management in England, Scotland and Wales: A population-scale descriptive analysis of trends in medication data

❗ Now published in Nature Medicine Caroline E Dale, Rohan Takhar, Raymond Carragher, Michalis Katsoulis, Fatemeh Torabi, Stephen Duffield, Seamus Kent, Tanja Mueller, Amanj Kurdi, Stuart McTaggart, Hoda Abbasizanjani, Sam Hollings, Andrew Scourfield, Ronan Lyons, Rowena Griffiths, Jane Lyons, Gareth Davies, Daniel Harris, Alex Handy, Mehrdad Alizadeh Mizani, Chris Tomlinson, Johan Thygesen, Mark Ashworth, Spiros Denaxas, Amitava Banerjee, Jonathan Sterne, Paul Brown, Ian Bullard, Rouven Priedon, Mamas A Mamas, Ann Slee, Paula Lorgelly, Munir Pirmohamed, Kamlesh Khunti, Andrew Morris, Cathie Sudlow, Ashley Akbari, Marion Bennie, Naveed Sattar, Reecha Sofat, CVD-COVID-UK Consortium (2023).

A nationwide deep learning pipeline to predict stroke and COVID-19 death in atrial fibrillation

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