• Skip to main content
  • Skip to header right navigation
  • Skip to site footer
CCAIM

CCAIM

Novel AI to transform healthcare

  • Home
  • About
    • Our Aims
    • ISAB Report
  • People
    • Leadership
    • Faculty
    • Associate Faculty
    • Joint Steering Committee
    • Independent Scientific Advisory Board
    • Staff
    • Our students
    • Affiliated clinical PhD Students
    • Visitors
  • Research
    • Papers
    • Breakthroughs
    • Software
      • AutoPrognosis
      • HyperImpute
      • Interpretability Suite
      • Synthcity
      • TemporAI
    • Demonstrators
    • Research Update: COVID-19
    • Blog
  • News
    • Latest News
    • COVID-19 News
  • Events
    • Seminar Series
    • WeCREATE
    • Inaugural Event
    • AI Clinic 2023
    • AI Clinic 2022
  • Summer School
    • Summer School 2023
      • Participate
      • Program
      • Topics
      • Industry
      • FAQ
    • Summer School 2022
  • Get involved
    • PhD Programmes
    • Clinical PhD Position
    • Partners
    • Connect

Alexander Gimson – ML in Transplantation Medicine

15 November 2022 by Andreas Bedorf

Dr Alexander Gimson, member of the CCAIM faculty, talks about the impact of machine learning on transplantation medicine.

For the inaugural CCAIM AI Clinic, Dr Alexander Gimson, Consultant Transplant Hepatologist at the Cambridge University Hospitals NHS Foundation Trust and member of the CCAIM faculty, talks about the impact of machine learning on transplantation medicine.

He also discusses the pioneering work which he has done with The van der Schaar lab in using ML and in particular individualised treatment effect estimation to improve organ allocation.

You can find the complete recording of the CCAIM AI Clinic from 10 November 2022 here.

If you are a practising clinician and are interested in staying up-to-date on the progress of AI and ML for healthcare, you can sign up for our Revolutionizing Healthcare sessions here.

For further reading on the topic, please refer to our following publications:

OrganITE: Optimal transplant donor organ offering using an individual treatment effect (NeurIPS 2020)

Jeroen Berrevoets, James Jordon, Ioana Bica, Alexander Gimson, Mihaela van der Schaar

Learning Queueing Policies for Organ Transplantation Allocation using Interpretable Counterfactual Survival Analysis (ICML 2021)

Jeroen Berrevoets, Ahmed M. Alaa, Zhaozhi Qian, James Jordon, Alexander Gimson, Mihaela van der Schaar

Closing the loop in medical decision support by understanding clinical decision-making: A case study on organ transplantation (NeurIPS 2021)

Yuchao Qin, Fergus Imrie, Alihan Hüyük, Daniel Jarrett, Alexander Gimson, Mihaela van der Schaar

Category: Interview, Organ transplantation, ResearchTag: AI Clinic, machine learning, Organ transplant, van der Schaar lab
Previous Post: « CCAIM at NeurIPS 2022
Next Post: External Validity of Machine Learning-based Prognostic Scores for Cystic Fibrosis – new publication »

Navigation

Home

News

About

University of Cambridge

  • University A-Z
  • Contact the University
  • Accessibility
  • Data Protection
  • Terms and conditions

Newsletter

Sign-up for updates on our research.

Follow us

  • Twitter
  • LinkedIn
  • YouTube

Copyright © 2023 CCAIM

Return to top

We are using cookies to give you the best experience on our website.

You can find out more about which cookies we are using or switch them off in settings.

CCAIM
Privacy Overview

This website uses cookies so that we can provide you with the best user experience possible. Cookie information is stored in your browser and performs functions such as recognising you when you return to our website and helping our team to understand which sections of the website you find most interesting and useful.

Strictly Necessary Cookies

Strictly Necessary Cookie should be enabled at all times so that we can save your preferences for cookie settings.

If you disable this cookie, we will not be able to save your preferences. This means that every time you visit this website you will need to enable or disable cookies again.

3rd Party Cookies

This website uses Google Analytics to collect anonymous information such as the number of visitors to the site, and the most popular pages.

Keeping this cookie enabled helps us to improve our website.

Please enable Strictly Necessary Cookies first so that we can save your preferences!