Workshop
Machine Learning for Global Health
Danielle Belgrave · Danielle Belgrave · Stephanie Hyland · Charles Onu · Nicholas Furnham · Ernest Mwebaze · Neil Lawrence
Keywords: Fairness Global health Machine learning for healthcare Technology transfer
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.
Schedule
Sat 5:45 a.m. - 6:00 a.m.
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Opening Remarks
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Opening
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Sat 6:00 a.m. - 6:30 a.m.
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Intended Use: A human-centered approach to developing ML applications for clinical practice
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Keynote
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SlidesLive Video Watch talk here: |
Stephanie Kuku 🔗 |
Sat 6:30 a.m. - 7:00 a.m.
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AI-augmented genomic pathogen surveillance - promises and pitfalls
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Keynote
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SlidesLive Video Watch talk here: |
Nicole Wheeler 🔗 |
Sat 7:00 a.m. - 7:20 a.m.
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Panel
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Panel
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Sat 7:20 a.m. - 7:35 a.m.
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Coffee Break
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Break
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Sat 7:35 a.m. - 7:45 a.m.
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An Unsupervised Learning Approach to Mitigate the Risk of Polio Recurrence in India
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Contributed Talk
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SlidesLive Video Watch talk here: |
Tushar Goswamy 🔗 |
Sat 7:45 a.m. - 7:55 a.m.
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Anonymous Survey System and Methodology to Enable COVID-19 Surveillance
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Contributed Talk
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SlidesLive Video Watch talk here: |
Natalie Davidson 🔗 |
Sat 7:55 a.m. - 8:05 a.m.
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Prediction of neonatal mortality in Sub-Saharan African countries using data-level linkage of multiple surveys
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Contributed Talk
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SlidesLive Video Watch talk here: |
Girmaw Abebe Tadesse 🔗 |
Sat 8:05 a.m. - 8:20 a.m.
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Contributed Talks Q&A + Panel
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Panel
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Live session. Watch with the viewer above or join the Zoom: |
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Sat 8:20 a.m. - 9:20 a.m.
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Posters
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Poster Session
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Each poster presenter is in a separate Zoom Meeting. Please click the link next to a poster to visit.
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Sat 9:20 a.m. - 10:30 a.m.
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Lunch
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Break
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Sat 10:30 a.m. - 11:00 a.m.
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Machine Learning and Epidemiology
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Keynote
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SlidesLive Video Watch talk here: |
Elaine Nsoesie 🔗 |
Sat 11:00 a.m. - 11:30 a.m.
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The Million Death Study and Systems for the Early Detection and Prevention of Infant Mortality in India
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Keynote
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Prabhat Jha 🔗 |
Sat 11:30 a.m. - 11:50 a.m.
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Panel Afternoon
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Panel
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Sat 11:50 a.m. - 12:50 p.m.
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Poster Session 2
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Poster Session
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Each poster presenter is in a separate Zoom Meeting. Please click the link next to a poster to visit.
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Sat 12:50 p.m. - 1:00 p.m.
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Using Machine Learning to Analyze and Provide Real-Time Access to all Published Clinical Trial Reports
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Contributed Talk
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SlidesLive Video Watch talk here: |
Iain Marshall 🔗 |
Sat 1:00 p.m. - 1:10 p.m.
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Automatic semantic segmentation for prediction of tuberculosis using lens-free microscopy images
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Contributed Talk
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SlidesLive Video Watch talk here: |
Dennis Núñez Fernández 🔗 |
Sat 1:10 p.m. - 1:20 p.m.
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Kernel-based antimicrobial resistance prediction from MALDI-TOF mass spectra
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Contributed Talk
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Caroline Weis 🔗 |
Sat 1:20 p.m. - 1:35 p.m.
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Contributed Talks Q&A + Panel
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Panel
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Live session. Watch with the viewer above or join the Zoom: |
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Sat 1:40 p.m. - 2:20 p.m.
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Breakout Session
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Breakout Session
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link
Live Session. Only on Zoom: |
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Sat 2:20 p.m. - 2:50 p.m.
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Synthesis of Breakout Session
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Breakout Session
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Live session. Watch only on Zoom: |
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Sat 2:50 p.m. - 3:05 p.m.
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Closing Remarks
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End
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