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/
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Fri 6:00 a.m. - 6:10 a.m.
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Opening Remarks
(Opening)
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Fri 6:10 a.m. - 6:30 a.m.
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Noémie Elhadad: Large scale characterization for health equity assessment
(Keynote)
Large scale characterization for health equity assessment |
Noemie Elhadad 🔗 |
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Fri 6:30 a.m. - 6:50 a.m.
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Mark Dredze: Reducing Health Disparities in the Future of Medicine
(Keynote)
SlidesLive Video » Health disparities in the United States are one of the largest factors in reducing the health of the population. Disparities means some groups have lower life expectancy, are dying at higher rates from COVID-19, and utilize less mental health services, to name just a few examples. The future of medicine will be based on Artificial Intelligence and new technological platforms that promise to improve outcomes and reduce cost. Our role as AI researchers should be to ensure that these new technologies also reduce health disparities. In this talk I will describe recent work showing how we can work to reduce health disparities in the future of medicine. By ensuring that our task, datasets, algorithms and evaluations are equitable and representative of all types of patients, we can ensure that the research we develop will reduce health disparities. |
Mark Dredze 🔗 |
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Fri 6:50 a.m. - 7:25 a.m.
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Panel with Noémie Elhadad and Mark Dredze
(Panel/QA)
Please use the video feed above to watch the panel. Post your questions at any time in RocketChat. |
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Fri 7:25 a.m. - 7:40 a.m.
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Break
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Click on "Open Link" to mingle with other attendees in the Gather.Town Lounge |
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Fri 7:40 a.m. - 8:00 a.m.
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Sponsor remarks: Modeling Pan-tumor, Personalized Healthcare Insights in a Multi-modal, Real-world Oncology Database with Sarah McGough
(Keynote)
Please use the video feed above to watch this talk. Post your questions at any time in RocketChat. |
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Fri 8:00 a.m. - 8:10 a.m.
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Spotlight A-1: "ML4H Auditing: From Paper to Practice"
(Spotlights)
SlidesLive Video » |
Luis Oala 🔗 |
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Fri 8:10 a.m. - 8:20 a.m.
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Spotlight A-2: "The unreasonable effectiveness of Batch-Norm statistics in addressing catastrophic forgetting across medical institutions"
(Spotlights)
SlidesLive Video » |
Sharut Gupta 🔗 |
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Fri 8:20 a.m. - 8:30 a.m.
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Spotlight A-3: "DeepHeartBeat: Latent trajectory learning of cardiac cycles using cardiac ultrasounds"
(Spotlights)
SlidesLive Video » |
Fabian Laumer 🔗 |
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Fri 8:30 a.m. - 9:30 a.m.
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Poster session A
(Poster session)
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Click on "Open Link" to attend the poster session in Gather.Town |
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Fri 9:30 a.m. - 12:30 p.m.
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Lunch
(Break)
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Click on "Open Link" to mingle with other attendees in the Gather.Town Lounge |
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Fri 12:30 p.m. - 12:50 p.m.
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Judy Gichoya: Operationalising Fairness in Medical Algorithms: A grand challenge
(Keynote)
SlidesLive Video » The year 2020 has brought into focus a second pandemic of social injustice and systemic bias with the disproportionate deaths observed for minority patients infected with COVID. As we observe an increase in development and adoption of AI for medical care, we note variable performance of the models when tested on previously unseen datasets, and also bias when the outcome proxies such as healthcare costs are utilized. Despite progressive maturity in AI development with increased availability of large open source datasets and regulatory guidelines, operationalizing fairness is difficult and remains largely unexplored. In this talk, we review the background/context for FAIR and UNFAIR sequelae of AI algorithms in healthcare, describe practical approaches to FAIR Medical AI, and issue a grand challenge with open/unanswered questions. |
Judy Gichoya 🔗 |
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Fri 12:50 p.m. - 1:10 p.m.
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Ziad Obermeyer: Explaining Pain Disparities
(Keynote)
SlidesLive Video » |
Ziad Obermeyer 🔗 |
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Fri 1:10 p.m. - 1:45 p.m.
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Panel with Judy Gichoya and Ziad Obermeyer
(Panel/QA)
Please use the video feed above to watch this panel. Post your questions at any time in RocketChat. |
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Fri 1:45 p.m. - 1:55 p.m.
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Spotlight B-1: "A Bayesian Hierarchical Network for Combining Heterogeneous Data Sources in Medical Diagnoses"
(Spotlights)
SlidesLive Video » |
Claire Donnat 🔗 |
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Fri 1:55 p.m. - 2:05 p.m.
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Spotlight B-2: "Assessing racial inequality in COVID-19 testing with Bayesian threshold tests"
(Spotlights)
SlidesLive Video » |
Emma Pierson 🔗 |
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Fri 2:05 p.m. - 2:15 p.m.
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Spotlight B-3: "EEG-GCNN: Augmenting Electroencephalogram-based Neurological Disease Diagnosis using a Domain-guided Graph Convolutional Neural Network"
(Spotlights)
SlidesLive Video » |
Neeraj Wagh 🔗 |
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Fri 2:15 p.m. - 3:15 p.m.
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Poster session B
(Poster session)
link »
Click on "Open Link" to attend the poster session in Gather.Town |
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Fri 3:15 p.m. - 3:30 p.m.
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Break
link »
Click on "Open Link" to mingle with other attendees in the Gather.Town Lounge |
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Fri 3:30 p.m. - 3:50 p.m.
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Andrew Ng: Practical limitations of today's deep learning in healthcare
(Keynote)
SlidesLive Video » Recent advances in training deep learning algorithms have demonstrated potential to accommodate the complex variations present in medical data. In this talk, I will describe technical advancements and challenges in the development and clinical application of deep learning algorithms designed to interpret medical images. I will also describe advances and current challenges in the deployment of medical imaging deep learning algorithms into practice. This talk presents work that is jointly done with Matt Lungren, Curt Langlotz, Nigam Shah, and several more collaborators. |
Andrew Ng 🔗 |
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Fri 3:50 p.m. - 4:10 p.m.
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Panel with Andrew Ng
(Panel/QA)
Please use the video feed above to watch this panel. Post your questions at any time in RocketChat. |
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Fri 4:10 p.m. - 4:20 p.m.
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Closing remarks
(Closing)
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