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Machine Learning for Global Health
Danielle Belgrave · Danielle Belgrave · Stephanie Hyland · Charles Onu · Nicholas Furnham · Ernest Mwebaze · Neil Lawrence

Sat Jul 18 05:45 AM -- 03:05 PM (PDT) @
Event URL: https://mlforglobalhealth.org/ »

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.

Author Information

Danielle Belgrave (Microsoft Research Cambridge)

Danielle Belgrave is a Machine Learning Researcher in the Healthcare AI Division at Microsoft Research Cambridge. She also has a (tenured) Research Fellowship at Imperial College London and received a Medical Research Council Career Development Award in Biostatistics (2015 – 2018). Her research focuses on integrating expert scientific knowledge to develop statistical machine learning models to understand disease progression over time, with the goal of identifying personalized disease management strategies. She has experience of applied machine learning for personalized health both within the pharmaceutical industry and academia.

Danielle Belgrave (Microsoft Research)
Stephanie Hyland (Microsft Research Cambridge)
Charles Onu (Mila and McGill)
Nicholas Furnham (London School of Hygiene and Tropical Medicine)
Ernest Mwebaze (Google AI)
Neil Lawrence (University of Cambridge)

Neil Lawrence is the DeepMind Professor of Machine Learning at the University of Cambridge and a Senior AI Fellow at the Alan Turing Institute.

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