Chris Tomlinson is an Anaesthetics & Intensive Care registrar undertaking a PhD at the UKRI UCL Centre for Doctoral Training in AI-enabled Healthcare Systems.
Combining technical expertise in epidemiology, data science and machine/deep learning with an extensive background in clinical medicine and physiological research he uses real world evidence to uncover new insights of critical relevance to patients, clinicians and policymakers. Most recently this has involved creating novel COVID-19 phenotypes from linked-EHR data of 57 million individuals in England (now published in Lancet Digital Health).
He is interested in the application of AI methodologies to disentangle the complex interactions between organ systems, diseases, individuals and healthcare services to advance our scientific understanding and translate knowledge into tangible benefits to patient care.
PhD Candidate: Artificial Intelligence enabled healthcare, 2021-2024
UKRI UCL Centre for Doctoral Training in AI-enabled Healthcare Systems, UCL Institute of Health Informatics
MRes Artificial Intelligence enabled healthcare, 2021
UKRI UCL Centre for Doctoral Training in AI-enabled Healthcare Systems, UCL Institute of Health Informatics
MRCA (Primary Fellowship), 2018
Royal College of Anaesthetists
Diploma in Medical Care of Catastrophes, 2016
The Worshipful Society of Apothecaries of London
Bachelor of Medicine & Surgery, 2014
University College London
Bachelor of Medical Sciences with Physiology (1st Class Hons), 2011
University College London
Should you be interested, you can follow my latest updates on Twitter @tomlincr:
Chris enjoys productive collaborations spanning computer science, health informatics and clinical medicine across UCL institutions, other universities within the UK and abroad as well as organisations such as Health Data Research UK, British Heart Foundation Data Science Centre, The Alan Turing Institute and Wellcome Trust.
He strongly believes in the value of multidisciplinary collaboration for delivering responsible innovation and impacts, particularly in complex areas such as healthcare and biomedicine, as well as relishing the learning opportunities that this provides.
This ethos is reflected in a wide range of co-authors, as visualised in the interactive network diagram below: