Dr Arturo Gonzalez-Izquierdo PhD

• PhD in Biostatistics, Imperial College London, 2008
• MSc in Modern Epidemiology, Imperial College London, 2003
• Specialisation (Hons) in Statistical Methods, Universidad Veracruzana, 1998
• BSc in Applied Statistics, Universidad Veracruzana, 1995

Arturo holds a PhD in Biostatistics and an MSc in Modern Epidemiology from Imperial College London, as well as undergraduate and postgraduate degrees in Statistics from Universidad Veracruzana, Mexico. His doctoral research focused on longitudinal risk prediction for hospitalisation using data from the Northern Finland Birth Cohort, laying the foundation for his career-long interest in the use of real-world data to understand disease trajectories and healthcare utilisation.

His research has consistently centred on the integration and analysis of electronic health records (EHRs) from diverse sources—primary care, secondary care, disease registries, and international datasets. Arturo has led data workflows for major platforms such as CALIBER, contributing to studies on multimorbidity, cardiovascular disease, neurological disorders, cancer, and child health. His methodological expertise includes cohort design, phenotyping, data curation, and reproducible analytics pipelines, with a strong emphasis on ethical data governance and transparency.

Dr Gonzalez-Izquierdo has extensive experience in curriculum design, module leadership, and teaching across postgraduate programmes in Health Data Science, Health Informatics, and Precision Medicine. He has developed and delivered courses on relational databases, SQL for biomedical researchers, data modelling, and EHR phenotyping. Arturo has also contributed to summer schools and short courses, including national training initiatives such as the National Centre for Research Methods and Asthma UK. His teaching philosophy emphasises practical skill development, reproducibility, and the integration of statistical and informatics principles for health research.

Arturo is an active postgraduate supervisor, currently supporting PhD and MSc students working on topics such as dementia progression, anticholinergic medication and cognitive decline, late-onset epilepsy, and cancer detection using EHRs. He has supervised students across UCL and Birmingham, contributing to projects that span epidemiology, clinical informatics, and statistical modelling. His supervision approach fosters independent thinking, methodological precision, and interdisciplinary engagement, preparing students for impactful careers in health data science.

Arturo works as an Electronic Health Records researcher at the Centre for Health Data Science using health data at a national level, linked across different sources within the healthcare system, to identify patient sub-populations and their multiple component phenotypes for the study of diseases and their potential risk factors. His current research activity is centred in optimal use of research data generated from EHR's in terms of determining the minimum set of data domains needed to better characterise the patient's healthcare pathway that integrates the clinical context as well as the process of healthcare provision.

At Birmingham, Arturo provides strategic guidance on data engineering and analytical standards for EHR-based research. He collaborates with multidisciplinary teams to translate complex data models into standardised formats, enabling robust and scalable research across clinical domains. His work supports the development of disease atlases, phenotyping algorithms, and population-based studies that inform both clinical practice and policy.

Arturo has authored numerous peer-reviewed publications in high-impact journals and has contributed to international comparative studies using large-scale administrative health data. His research is characterised by a commitment to methodological rigour, interdisciplinary collaboration, and the advancement of data-driven approaches to public health.