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Register your place for this Inaugural Lecture on Microsoft Forms.

Join us to celebrate a special milestone for our new professors and hear about their inspiring career journeys. Doors for this event will open on 16.45 (BST), with the lectures to commence at 16.50. A drinks reception will be held at 17.30 immediately after the lecture.

Professor Abdel Douiri

Challenges, Methods, And Future Directions of Prediction: The Case of Recovery After Stroke


Predictive methods are based on statistical models or machine learning techniques to estimate the risk of future outcomes based on historical data. It offers the opportunity of targeted early intervention and more specific information for patients and carers. In this talk, we discuss the characteristics and challenges in building predictive models using electronic health records, provide an overview of current recovery curve predictions, and outline several directions of future work that are relevant to monitoring recovery after stroke. 


Abdel is an applied mathematician by background, MSc at Paris-Sorbonne University and PhD at Paris-Orsay University. He has been involved in a variety of clinical research with world leading research groups in UK and France, and in both academia and industry. His research interests include epidemiology of long-term conditions, predictive medicine and clinical trials design and methodology. Since 2009, at King’s College London he teaches biostatistics and epidemiology for both undergraduate medical students and postgraduate courses for Public Health and Pharmaceutical Sciences. Abdel serves on several clinical trial committees as well as on UK-MRC Experimental Medicine funding Committee and the UK-NIHR Senior Clinical and Practitioner Research Award Selection Committee. He is a fellow of the Royal Statistical Society and statistical/associate editor of several scientific journals.

At this event

Abdel Douiri

Chair in Medical Statistics and Clinical Trials

Event details

Lecture Theatre 1, New Hunts House
Guy’s Campus
Great Maze Pond, London SE1 1UL