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Workshop
Fri Dec 11 06:00 AM -- 04:20 PM (PST)
Machine Learning for Health (ML4H): Advancing Healthcare for All
Stephanie Hyland · Allen Schmaltz · Charles Onu · Ehi Nosakhare · Emily Alsentzer · Irene Y Chen · Matthew McDermott · Subhrajit Roy · Benjamin Akera · Dani Kiyasseh · Fabian Falck · Griffin Adams · Ioana Bica · Oliver J Bear Don't Walk IV · Suproteem Sarkar · Stephen Pfohl · Andrew Beam · Brett Beaulieu-Jones · Danielle Belgrave · Tristan Naumann





Workshop Home Page

The application of machine learning to healthcare is often characterised by the development of cutting-edge technology aiming to improve patient outcomes. By developing sophisticated models on high-quality datasets we hope to better diagnose, forecast, and otherwise characterise the health of individuals. At the same time, when we build tools which aim to assist highly-specialised caregivers, we limit the benefit of machine learning to only those who can access such care. The fragility of healthcare access both globally and locally prompts us to ask, “How can machine learning be used to help enable healthcare for all?” - the theme of the 2020 ML4H workshop.

Participants at the workshop will be exposed to new questions in machine learning for healthcare, and be prompted to reflect on how their work sits within larger healthcare systems. Given the growing community of researchers in machine learning for health, the workshop will provide an opportunity to discuss common challenges, share expertise, and potentially spark new research directions. By drawing in experts from adjacent disciplines such as public health, fairness, epidemiology, and clinical practice, we aim to further strengthen the interdisciplinarity of machine learning for health.

See our workshop for more information: https://ml4health.github.io/

Opening Remarks (Opening)
Noémie Elhadad: Large scale characterization for health equity assessment (Keynote)
Mark Dredze: Reducing Health Disparities in the Future of Medicine (Keynote)
Panel with Noémie Elhadad and Mark Dredze (Panel/QA)
Break
Sponsor remarks: Modeling Pan-tumor, Personalized Healthcare Insights in a Multi-modal, Real-world Oncology Database with Sarah McGough (Keynote)
Spotlight A-1: "ML4H Auditing: From Paper to Practice" (Spotlights)
Spotlight A-2: "The unreasonable effectiveness of Batch-Norm statistics in addressing catastrophic forgetting across medical institutions" (Spotlights)
Spotlight A-3: "DeepHeartBeat: Latent trajectory learning of cardiac cycles using cardiac ultrasounds" (Spotlights)
Poster session A (Poster session)
Lunch (Break)
Judy Gichoya: Operationalising Fairness in Medical Algorithms: A grand challenge (Keynote)
Ziad Obermeyer: Explaining Pain Disparities (Keynote)
Panel with Judy Gichoya and Ziad Obermeyer (Panel/QA)
Spotlight B-1: "A Bayesian Hierarchical Network for Combining Heterogeneous Data Sources in Medical Diagnoses" (Spotlights)
Spotlight B-2: "Assessing racial inequality in COVID-19 testing with Bayesian threshold tests" (Spotlights)
Spotlight B-3: "EEG-GCNN: Augmenting Electroencephalogram-based Neurological Disease Diagnosis using a Domain-guided Graph Convolutional Neural Network" (Spotlights)
Poster session B (Poster session)
Break
Andrew Ng: Practical limitations of today's deep learning in healthcare (Keynote)
Panel with Andrew Ng (Panel/QA)
Closing remarks (Closing)