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Sat Jul 18 05:45 AM -- 03:05 PM (PDT)
Machine Learning for Global Health
Danielle Belgrave · Danielle Belgrave · Stephanie Hyland · Charles Onu · Nicholas Furnham · Ernest Mwebaze · Neil Lawrence

Workshop Home Page

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.

Opening Remarks (Opening)
Intended Use: A human-centered approach to developing ML applications for clinical practice (Keynote)
AI-augmented genomic pathogen surveillance - promises and pitfalls (Keynote)
Coffee Break (Break)
An Unsupervised Learning Approach to Mitigate the Risk of Polio Recurrence in India (Contributed Talk)
Anonymous Survey System and Methodology to Enable COVID-19 Surveillance (Contributed Talk)
Prediction of neonatal mortality in Sub-Saharan African countries using data-level linkage of multiple surveys (Contributed Talk)
Contributed Talks Q&A + Panel (Panel)
Posters (Poster Session)
Lunch (Break)
Machine Learning and Epidemiology (Keynote)
The Million Death Study and Systems for the Early Detection and Prevention of Infant Mortality in India (Keynote)
Panel Afternoon (Panel)
Poster Session 2 (Poster Session)
Using Machine Learning to Analyze and Provide Real-Time Access to all Published Clinical Trial Reports (Contributed Talk)
Automatic semantic segmentation for prediction of tuberculosis using lens-free microscopy images (Contributed Talk)
Kernel-based antimicrobial resistance prediction from MALDI-TOF mass spectra (Contributed Talk)
Contributed Talks Q&A + Panel (Panel)
Breakout Session
Synthesis of Breakout Session (Breakout Session)
Closing Remarks (End)