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This workshop will launch a new platform for open medical imaging datasets. Labeled with ground-truth outcomes curated around a set of unsolved medical problems, these data will deepen ways in which ML can contribute to health and raise a new set of technical challenges.
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Tue 7:00 a.m. - 7:15 a.m.
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Machine learning from ground truth: introductory remarks
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Introductory remarks
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SlidesLive Video » Welcome and Opening Remarks: Eric Schmidt |
Ziad Obermeyer · Sendhil Mullainathan · Katy Haynes · Eric Schmidt 🔗 |
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Tue 7:15 a.m. - 7:20 a.m.
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Transition break
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Tue 7:20 a.m. - 8:00 a.m.
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What are “meaningful” ML datasets and the opportunities and challenges in creating them?
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Panel
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SlidesLive Video » This panel will explore the challenges and opportunities of finding and building meaningful datasets within health systems leveraging concrete examples the panelists have in their experience of building and working with healthcare datasets. Featured panelists include: Judy Gichoya, Assistant Professor of Radiology and Imaging, Emory; Matt Lungren, Director of the Stanford Center for Artificial Intelligence in Medicine & Imaging; Ari Robiczek, Chief Medical Analytics Officer, Providence St. Joseph Health |
Judy Wawira · Matthew Lungren · Elaine Nsoesie · Ari Robicsek 🔗 |
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Tue 8:00 a.m. - 8:05 a.m.
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Transition break
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Tue 8:05 a.m. - 9:00 a.m.
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Spotlight talks: new datasets and research finalists
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Spotlight talks - accepted papers
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SlidesLive Video » Talks will feature new datasets and selected research submissions. Features may include electrocardiogram waveform (ECG) dataset for heart attack prediction and prediction of sudden cardiac death, and a dataset of breast cancer biopsy slides for insight into the cancer staging process. |
Roxana Daneshjou · Sharmita Dey · Sabri Boughorbel · TestMatt TestMcDermott · Daniel Gedon 🔗 |
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Tue 9:00 a.m. - 9:30 a.m.
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Lunch break
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Tue 9:30 a.m. - 10:00 a.m.
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A conversation around medical mysteries, featuring Kevin Volpp & Eric Topol
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Discussion panel
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link »
SlidesLive Video » Through a moderated conversation, panlists Eric Topol and Kevin Volpp will explore the biggest barriers to meaningful research for medical unknowns that we still face, and what can help. This panel will touch on Kevin Volpp's story; learn more here: |
Kevin G Volpp · Eric Topol 🔗 |
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Tue 10:00 a.m. - 10:15 a.m.
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Transition break
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Tue 10:15 a.m. - 11:00 a.m.
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Data science for healthcare in academia and government
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Panel
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SlidesLive Video » This focus of this session will be twofold, leveraging the panelists’ experience: 1) the importance of well-structured data-science programs within academia as a way to ensure a well-trained, robust pipeline of up-and-coming researchers, and provide the data infrastructure necessary to support ground breaking research and 2) examine what can be done at the federal level to encourage better data sharing and research. Featured panelists include Aneesh Chopra, President of CareJourney, first U.S. Chief Technology Officer; Jennifer Chayes, Associate Provost, Computing, Data Science, and Society, UC Berkeley; Kate Baicker, Dean and Emmett Dedmon Professor, Chicago Harris School of Public Policy |
Katherine Baicker · Jennifer Chayes · Aneesh P Chopra 🔗 |
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Tue 11:00 a.m. - 11:15 a.m.
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Transition break
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Tue 11:15 a.m. - 12:00 p.m.
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Data Opportunities: unsolved medical problems and where new data can help
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Panel
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SlidesLive Video » This panel will begin by introducing three critical medical issues that drive mortality despite years of research: cancer, sudden cardiac death, maternal mortality. The moderator will facilitate a group conversation about how the panelists approach working on issues like these in their research, as well as challenges and opportunities in applying new data and ML tools to similar issues in medicine. Featured panelists include Regina Barzilay, Distinguished Professor for AI and Health, EECS, MIT; Marzyeh Ghassemi, Assistant Professor, EECS and IMES, MIT, Faculty Member, The Vector Insitute Elaine Nsoesie, Assistant Professor, Boston University School of Public Health; Emma Pierson, Assistant Professor of Computer Science, Jacobs Technion-Cornell Institute at Cornell Tech |
Bin Yu · Regina Barzilay · Marzyeh Ghassemi · Emma Pierson 🔗 |
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Tue 12:00 p.m. - 12:05 p.m.
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Transition break
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Tue 12:05 p.m. - 12:15 p.m.
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Pain Prediction in Neurological Spine Disease Patient Using Digital Phenotyping
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Poster
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Poster session featuring accepted papers |
Aakanksha Rana 🔗 |
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Tue 12:15 p.m. - 12:30 p.m.
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Assessing Changes in BNP from Chest Radiographs using Convolutional Neural Networks
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Poster
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Allen Ding 🔗 |
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Tue 12:30 p.m. - 12:45 p.m.
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Disability prediction in multiple sclerosis using performance outcome measures and demographic data
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Poster
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Diana Mincu 🔗 |
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Tue 12:45 p.m. - 12:50 p.m.
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Transition break
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Tue 12:50 p.m. - 1:20 p.m.
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What problems get funded in computational medicine?
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Panel
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SlidesLive Video » This session will be a conversation-style panel focusing on how funding decisions are made in computational medicine, what funders and other stakeholders can do to increase data sharing and research, and will culminate with an announcement of a new computational medicine funding collaborative. |
Daniel Yang · Katy Haynes 🔗 |
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Tue 1:20 p.m. - 1:25 p.m.
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Transition break
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Tue 1:25 p.m. - 1:55 p.m.
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Nightingale Open Science platform launch and video demonstration
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Live talk
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SlidesLive Video » This session will announce the launch of Nightingale Open Science and feature a demonstration of the product. Today, health data are mostly locked up in small sandboxes, controlled by a handful of private companies or well-resourced researchers. Nightingale Open Science aims to unlock those data, securely and ethically, and make them available for the public good. Just as ImageNet jumpstarted the field of machine vision, Nightingale seeks to build a community of researchers working in a new scientific field: ‘computational medicine.’ Nightingale OS datasets are curated around medical mysteries—heart attack, cancer metastasis, cardiac arrest, bone aging, Covid-19—where machine learning can be transformative. |
Josh Risley 🔗 |
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Tue 1:55 p.m. - 2:00 p.m.
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Transition break
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Tue 2:00 p.m. - 2:10 p.m.
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Closing remarks
SlidesLive Video » |
Ziad Obermeyer 🔗 |
Author Information
Katy Haynes (Nightingale Open Science)
Ziad Obermeyer (UC Berkeley)
Emma Pierson (Microsoft Research)
Marzyeh Ghassemi (University of Toronto / Vector Institute)
Matthew Lungren (Stanford)
Sendhil Mullainathan (University of Chicago)
Matthew McDermott (MIT)
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