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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

Fri Dec 11 06:00 AM -- 04:20 PM (PST) @
Event URL: https://ml4health.github.io/2020/ »

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/

Author Information

Stephanie Hyland (Microsoft Research)
Allen Schmaltz (Harvard University)
Charles Onu (McGill University)
Ehi Nosakhare (Microsoft)
Emily Alsentzer (MIT)
Irene Y Chen (MIT)

Irene is a PhD student at MIT focusing on applications on health care and fairness. She did her undergrad at Harvard where I studied applied math and computational engineering. Before starting at MIT, she worked for two years at Dropbox as a data scientist and machine learning engineer.

Matthew McDermott (MIT)
Subhrajit Roy (Google)
Benjamin Akera (Mila - Quebec AI Institute)
Dani Kiyasseh (University of Oxford)
Fabian Falck (University of Oxford)
Griffin Adams (Columbia University)

I am an NLP researcher with a focus on text generation of clinical data. After completing a masters in Computational Data Science at Carnegie Mellon's Language Technologies Institute (LTI), I worked at Flatiron Health where I developed and deployed algorithms to extract clinical information from unstructured oncology data at scale. I introduced deep learning to the company and architected a generalized model that improves the status quo of information extraction from large-scale longitudinal clinical notes. I am now a computer science PhD student at Columbia University with Noemie Elhadad. My research focuses on controllable factual text generation of clinical narratives.

Ioana Bica (University of Oxford)
Oliver J Bear Don't Walk IV (Columbia University)
Suproteem Sarkar (Harvard)
Stephen Pfohl (Stanford University)
Andrew Beam (Harvard)
Brett Beaulieu-Jones (Harvard Medical School)
Danielle Belgrave (Microsoft Research)
Tristan Naumann (Microsoft Research)

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