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Machine Learning for Health (ML4H): Advancing Healthcare for All
Stephanie Hyland · Emily Alsentzer · Andrew Beam · Brett Beaulieu-Jones · Danielle Belgrave · Allen Schmaltz · Irene Y Chen · Anna Goldenberg · Matthew McDermott · Tristan Naumann · Charles Onu

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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)
Keynote 1 (Keynote)
Noemie Elhadad
Keynote 2 (Keynote)
Mark Dredze
Panel with keynotes 1-2 (Panel/QA)
Sponsor remarks (Keynote)
Spotlights A (Spotlights)
Poster session A (Poster session)
Lunch (Break)
Keynote 3 (Keynote)
Judy Gichoya
Keynote 4 (Keynote)
Ziad Obermeyer
Panel with keynotes 3-4 (Panel/QA)
Spotlights B (Spotlights)
Poster session B (Poster session)
Keynote 5 (Keynote)
Andrew Ng
Panel with keynote 5 (Panel/QA)
Closing remarks (Closing)