Antonella Delmestri
PhD
Associate Professor of Health Data Science
- EMA-led DARWIN EU® initiative PI data partner
- EHDEN UK data science technical lead
- OHDSI OMOP common data model specialist
- Alan Turing Alumnus
- CPRD fob holder for NDORMS
I lead a Data Science group, responsible for the modelling and standardisation of real-world data (RWD) routinely collected by GPs, hospitals and registries, and used in observational research to produce clinical evidence. I am a collaborator of EMA-led DARWIN® EU initiative, EHDEN, OHDSI, and an expert in the OMOP common data model. I am an Alan Turing Alumnus and I serve as member of the Turing Research Ethics (TREx) panel.
My research is focussed on:
- assessment, evaluation and curation of RWD
- impact of RWD quality on medical research output
- modelling and standardisation of RWD
- measures of similarity/distance between RWD and their standardised versions
- measurement and enhancement of algorithmic complexity in the context of RWD engineering
- research ethics, research quality, reproducibility, consistency and transparency
BIOGRAPHY
I graduated in Computer Science at the University of Udine and completed my PhD in Information and Communication Technologies at the University of Trento (Italy). I have worked for several years for companies developing firmware and software for electronic devices.
In 2006 I moved to the U.K. to join the Clinical Trial Service Unit (CTSU) at Oxford University. In 2013 I joined NDORMS to work in observational research. In 2018 I joined the European Health Data and Evidence Network (EHDEN) collaboration and in 2019 I started leading the OHDSI UK Data Science technical activities. Between 2021-24 I was a Fellow at the Alan Turing Institute. Since 2022 I am a PI Data Partner in the EMA-led DARWIN® EU initiative.
Recent publications
Association between SARS-CoV-2 and diabetes: A population-based cohort study using the UK CPRD GOLD database.
Journal article
Spivakovsky P. et al, (2026), J Diabetes Metab Disord, 25
Mitigating health inequities with machine learning: a nationwide cohort study developing and evaluating ethnicity-specific cardiovascular risk prediction models across 19 ethnically diverse populations in 2.5 million individuals with COVID-19
Journal article
Allery F. et al, (2026), Lancet Digital Health
Correction: Academic impact and research data utilisation of the clinical practice research datalink: scientometric analyses.
Journal article
Pineda-Moncusí M. et al, (2026), Eur J Epidemiol
Effectiveness and waning of the fourth dose mRNA COVID-19 vaccines for the prevention of SARS-CoV2 infection related hospitalisations and deaths.
Journal article
Li X. et al, (2026), J Infect
Characterising intra-articular corticosteroid injections use in UK primary care: a retrospective cohort study between 2005 and 2020
Journal article
Samuels F. et al, (2026), Rheumatology Advances in Practice
DrugUtilisation: An R Package to Support Drug Utilisation Research Using the OMOP Common Data Model.
Journal article
Guo Y. et al, (2026), Pharmacoepidemiol Drug Saf, 35
Validating primary and secondary healthcare resource use and costs estimates in OMOP-mapped vs source CPRD-HES data in the UK
Conference paper
Fabiano G. et al, (2026), Value in Health, 29
Prescription cascades associated with acetylcholinesterase inhibitors use: a high-throughput sequence symmetry analysis
Journal article
Newby D. et al, (2026), Age and Ageing