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Workshop
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) @ None
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.

Sat 5:45 a.m. - 6:00 a.m.
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Sat 6:00 a.m. - 6:30 a.m.
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Stephanie Kuku
Sat 6:30 a.m. - 7:00 a.m.
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Nicole Wheeler
Sat 7:00 a.m. - 7:20 a.m.
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Sat 7:20 a.m. - 7:35 a.m.
Coffee Break (Break)
Sat 7:35 a.m. - 7:45 a.m.
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Tushar Goswamy
Sat 7:45 a.m. - 7:55 a.m.
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Natalie Davidson
Sat 7:55 a.m. - 8:05 a.m.
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Girmaw Abebe Tadesse
Sat 8:05 a.m. - 8:20 a.m.
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Sat 8:20 a.m. - 9:20 a.m.

Each poster presenter is in a separate Zoom Meeting. Please click the link next to a poster to visit.

  • A Bayesian Updating Scheme for Global Pandemics. [ protected link dropped ]

  • A Federated Learning Framework for Healthcare IoT devices. [ protected link dropped ]

  • MOSQUITO-NET: A deep learning based CADx system for malaria diagnosis. [ protected link dropped ]

  • A scoping review on mental health and machine learning in low-resource settings. [ protected link dropped ]

  • Synthesizing Wearable ECG Data for Future Deep Space Missions. [ protected link dropped ]

  • Using Capsule Neural Network to detect Tuberculosis in microscopic images. [ protected link dropped ]

  • Design Considerations for High Impact, Automated Echocardiogram Analysis. [ protected link dropped ]

  • A novel approach for predicting epidemiological forecasting parameters based on real-time signals and Data Assimilation. [ protected link dropped ]

  • Gene Expression Imputation with Generative Adversarial Imputation Nets. [ protected link dropped ]

  • Simulation-Based Inference for Global Health Decisions. [ protected link dropped ]

  • Towards uncertainty representations for decision support system patient referrals in healthcare contexts. [ protected link dropped ]

  • SIRNet: Understanding Social Distancing Measures with Hybrid Neural Network Model for COVID-19 Infectious Spread. [ protected link dropped ]

  • Non-Pharmaceutical Intervention Discovery with Topic Modeling. [ protected link dropped ]

  • Sequential Decision Making in Resource Constrained Global Health Settings. [ protected link dropped ]

  • Measuring the Role of Income Shocks on the Health of Low-Income Families. [ protected link dropped ]

  • PET-guided Attention Network for Segmentation of Lung Tumors from PET/CT images that accounts for missing PET images. [ protected link dropped ]

  • The Causal Effect of Stay-At-Home Orders: a synthetic control study in the San Francisco Bay Area. [ protected link dropped ]

  • An Unsupervised Learning Approach to Mitigate the Risk of Polio Recurrence in India. [ protected link dropped ]

Sat 9:20 a.m. - 10:30 a.m.
Lunch (Break)
Sat 10:30 a.m. - 11:00 a.m.
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Elaine Nsoesie
Sat 11:00 a.m. - 11:30 a.m.
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Prabhat Jha
Sat 11:30 a.m. - 11:50 a.m.
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Sat 11:50 a.m. - 12:50 p.m.

Each poster presenter is in a separate Zoom Meeting. Please click the link next to a poster to visit.

  • Automatic semantic segmentation for prediction of tuberculosis using lens-free microscopy images [ protected link dropped ]

  • NUCLEI-NET: A Fully Convolutional Neural Network for Medicinal Drugs Discovery [ protected link dropped ]

  • Towards User Friendly Medication Mapping Using Entity-Boosted Twin Modeling [ protected link dropped ]

  • Machine Learning to Assess the Association of H. Pylori Infection and Gastric and Oesophageal Cancer by Detecting Western Blot Protein Bands [ protected link dropped ]

  • Prediction of neonatal mortality in Sub-Saharan African countries using data-level linkage of multiple surveys [ protected link dropped ]

  • Mortality Risk Score for Critically Ill Patients with Viral or Unspecified Pneumonia: Assisting Clinicians with COVID-19 ECMO Planning [ protected link dropped ]

  • Bayesian inference of between-population variation in COVID-19 dynamics [ protected link dropped ]

  • Transfer Learning for Pandemic Forecasting [ protected link dropped ]

  • A COVID-19 Spread Prediction System Using Deep Neural Networks with Important Hybrid Features Extracted from Relevant Multi-source Data [ protected link dropped ]

Sat 12:50 p.m. - 1:00 p.m.
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Iain Marshall
Sat 1:00 p.m. - 1:10 p.m.
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Dennis Núñez Fernández
Sat 1:10 p.m. - 1:20 p.m.
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Caroline Weis
Sat 1:20 p.m. - 1:35 p.m.
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Sat 1:40 p.m. - 2:20 p.m.
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Sat 2:20 p.m. - 2:50 p.m.
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Sat 2:50 p.m. - 3:05 p.m.
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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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