Machine Learning for Global Health

Machine learning is transforming many aspects of human endeavour. Can we direct it towards the global health of humanity?

Workshop Date: July 18, 2020
Venue: Virtual

Announcements
Jun 17, 2020 – Notifications for accepted work have been sent out – thanks to all who submitted! See the accepted posters and spotlights here.

Mar 24, 2020 -ICML 2020 will now be a fully remote conference – see the letter from the organisers. This means we will have a virtual workshop!

Mar 10, 2020 – ML for Global Health was accepted as an official workshop at the International Conference on Machine Learning (ICML).

Overview

Machine learning is increasingly being applied to problems in the healthcare domain. However, there is a risk that the development of machine learning models for improving health remain focused within areas and diseases which are more economically incentivised and resourced. This presents the risk that as research and technological entities aim to develop machine-learning- assisted consumer healthcare devices, or bespoke algorithms for their populations within a certain geographical region, that the challenges of healthcare in resource-constrained settings will be overlooked. The predominant research focus of machine learning for healthcare in the “economically advantaged” world means that there is a skew in our current knowledge of how machine learning can be used to improve health on a more global scale – for everyone. This workshop aims to draw attention to the ways that machine learning can be used for problems in global health, and to promote research on problems outside high-resource environments.

Global health is a multidisciplinary area of study which focuses on “improving health and achieving equity in health for all people worldwide”. “We should not restrict global health to health-related issues that literally cross international borders. Rather, in this context, global refers to any health issue that concerns many countries or is affected by transnational determinants, such as climate change or urbanisation, or solutions, such as polio eradication.”[1]. 

[1] Koplan, J.P., Bond, T.C., Merson, M.H., Reddy, K.S., & Board, F.T. (2009). Towards a common definition of global health. The Lancet, 373, 1993-1995.

Important Dates

Submission deadline: May 13, 2020
Author Notification: June 17, 2020
Workshop date: July 18, 2020

Organizers

  • Danielle Belgrave, Principal Researcher in the Healthcare Intelligence Team at Microsoft Research Cambridge. 
  • Stephanie Hyland, Senior Researcher in the Healthcare Intelligence Team at Microsoft Research Cambridge. 
  • Charles C Onu, Vanier Doctoral Scholar at McGill University and Mila – the Québec AI Institute.
  • Ernest Mwebaze, Research Scientist at Google AI, Accra. 
  • Nicholas Furnham, Associate Professor at the London School of Hygiene and Tropical Medicine. 
  • Neil Lawrence, DeepMind Professor of Machine Learning at the University of Cambridge, Senior AI Fellow at the Alan Turing Institute.

Sponsors

Are you interested in sponsoring ML4GH? Reach out to mlforglobalhealth@gmail.com