Chris Tomlinson

Chris Tomlinson

MBBS BSc MRes DMCC MRCA

UK Health Data & AI Lead

Honorary Senior Research Fellow in Clinical Foundation Models

LifeArc

UCL Institute of Health Informatics

Health Data Research UK

Biography

Chris is a curiosity-driven physician scientist with over a decade of experience in healthcare, spanning physiological research and clinical medicine, as well as quantitative methods in epidemiology and machine learning. His work has been featured in top medical journals (Nature Medicine, Circulation, BMJ, Lancet Digital Health) and informed policy at home and abroad.

He is excited about the ability of artificial intelligence to flexibly integrate, structure and summarise insights from both traditional knowledge repositories and observational data, to scalably advance our understanding of health and disease, and enable us to address the fundamental challenges of precision medicine: diagnosis, prognosis and treatment response.

Interests

  • Clinical foundation models: Harnessing real-world EHR data, codifying domain knowledge, downstream use for precision medicine, trial emulation and population health, multimodality
  • Real-world evidence: I prioritise working with routinely-collected healthcare data to ensure my research is of direct relevance to patients, clinicians and policymakers
  • Causal inference: for evidence generation from observational data and to learn better representations
  • Uncertainty quantification: for clinical evaluation and scientific discovery, through assessing robustness to distributional shift and performance in inductive learning settings
  • Phenomics: Defining clinically-relevant phenotypes at scale to unlock healthcare data for research & patient benefit

Education

  • 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 of the Royal College of Anaesthetists 2018
    Royal College of Anaesthetists
  • DMCC: Diploma in Medical Care of Catastrophes 2016
    The Worshipful Society of Apothecaries of London
  • MBBS: Bachelor of Medicine & Surgery 2014
    University College London
  • BSc: Bachelor of Medical Sciences with Physiology (1st Class Hons) 2011
    University College London

Projects

Foresight: a generative AI model of patient trajectories across the COVID-19 pandemic
GPT model trained from scratch on EHR codes of 57 million individuals for universal risk prediction and trial emulation.
NHS DART Internship: Graph Representation Learning
Representation learning for EHR data, exploring graph structures and semantic embeddings applied to national-scale datasets
NHS England Data Science PhD Internships: Project Brief
UCL-GSK Phenomics Hub
Phenotyping at scale across diverse biobank cohorts to power genomic & proteomic analyses for target identification, drug discovery, and precision medicine.
Disease Atlas
Underpinned by the needs of patients, clinicians and researchers, the Disease Atlas is an ambitious project involving the generation of a systematic, data-driven knowledge across all common and rare diseases. Using newly available nationwide data on 56 million people the Atlas is generating novel comparative insights of the health needs of patients, the care provided, and the research that is carried out.
Automated Assessment of Vascular Perfusion (Alan Turing Institute Data Study Group)
Deep-learning based analysis of sidestream dark field (SDF) imaging videos to quanitfy vascular perfusion in ICU patients.
Alan Turing Institute Data Study Group
CVD-COVID-UK/COVID-IMPACT Consortium
Contributing clinical experience, data wrangling and analytical skills to development of COVID-19 Severity phenotypes from linked EHR data for 54 million patients in England.
HDR-UK Descriptive Paper Phenotyping Paper
Xtreme Everest 2
Large expedition to Everest Base Camp seeking to characterise acclimatisation to environmental hypoxia in Sherpa’s & Westerners to identify biomarkers of (mal-) adaptation that may inspire novel diagnostic/therapeutic targets for the cellular hypoxia seen in critical illness.
Xtreme Everest Descriptive Paper
Innovation in Transtracheal Airway Design
Established ‘Innovation in Anaesthesia’ BSc project leading to multidisciplinary and industrial collaboration
UCL Centre for Anaesthesia

Collaborators

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 and industry partners such as GSK, Databricks and AWS.

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:

Featured Presentations

K Brown, Chris Tomlinson
Jul 19, 2023 12:00 AM Online
Hospital admissions linked to SARS-CoV-2 infection in children and adolescents: cohort study of 3.2 million first ascertained infections in England

For July’s BHF Data Science Centre webinar we were joined by Professor Kate Brown from Great Ormond Street Hospital (GOSH) and Dr Chris Tomlinson from University College London (UCL). They speak about findings from a recent paper, published on 5th July 2023, outlining the impact of COVID-19 on children across England. This study - undertaken as part of the BHF Data Science Centre CVD-COVID-UK/COVID-IMPACT Consortium – looked at 12 million childhood health records across England during the height of the pandemic, and analysed 3.2 million cases of coronavirus infection. The study reveals the extent of severe outcomes in children during the pandemic and could inform public health policies.

Video
Chris Tomlinson
Mar 16, 2022 12:00 AM Online
BHF Data Science Centre Research Showcase: Phenotyping COVID-19: Insights from linked data for 56 million individuals

Prof Nick Lemoine, Prof Martin Gibson, Prof Cathie Sudlow, Dr Angela Wood, Dr Venexia Walker, Dr Johan Thygesen, Chris Tomlinson, Lynn Morrice
Sep 14, 2021 12:00 AM Online
NIHR & BHF Data Science Centre Webinar: Data Linkage for COVID-19 Research

A joint webinar hosted by National Institute for Health Research (NIHR) and British Heart Foundation (BHF) Data Science Centre for NIHR supported COVID-19 studies, to highlight how to utilise and access routinely-collected healthcare data via NHS Digital’s Trusted Research Environment, the UK’s largest linked health data research asset.

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News

Should you be interested, you can follow my latest updates on Twitter @tomlincr:

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