ML4H: Machine Learning for Health

ML4H: Machine Learning for Health

Workshop at NeurIPS 2020

Schedule

The workshop will be held December 11 virtually. Times are listed in EST.

Our videos are available on SlidesLive including keynotes, panels, and spotlight presentations.

Previously, the NeurIPS workshop page contained live video, chat links, and the most updated schedule. We also created a Livestream Guide for Attendees

The workshop adheres to to the NeurIPS Code of Conduct. Please contact the organization committee with any concerns.

Videos will be posted here after the conference. Feedback and interest in organizing can be submitted here

09:00 - 09:10 Opening remarks with Stephanie Hyland
09:10 - 09:30 Noémie Elhadad: Large scale characterization for health equity assessment
09:30 - 09:50 Mark Dredze: Reducing Health Disparities in the Future of Medicine
09:50 - 10:25 Discussion with Noémie Elhadad and Mark Dredze
10:25 - 10:40 Social Break
10:40 - 11:00 Sponsorship remarks: Modeling Pan-tumor, Personalized Healthcare Insights in a Multi-modal, Real-world Oncology Database with Sarah McGough
11:00 - 11:30 Spotlights A
  • Luis Oala: "ML4H Auditing: From Paper to Practice"
  • Sharut Gupta: "The unreasonable effectiveness of Batch-Norm statistics in addressing catastrophic forgetting across medical institutions"
  • Fabian Laumer: ""DeepHeartBeat: Latent trajectory learning of cardiac cycles using cardiac ultrasounds"
11:30 - 12:30 Poster Session A
12:30 - 15:30 Lunch
15:30 - 15:50 Judy Gichoya: Operationalising Fairness in Medical Algorithms: A grand challenge
15:50 - 16:10 Ziad Obermeyer: Explaining Pain Disparities
16:10 - 16:45 Discussion with Judy Gichoya and Ziad Obermeyer
16:45 - 17:15 Spotlights B
  • Claire Donnat: "A Bayesian Hierarchical Network for Combining Heterogeneous Data Sources in Medical Diagnoses"
  • Emma Pierson: "Assessing racial inequality in COVID-19 testing with Bayesian threshold tests"
  • Neeraj Wagh: "EEG-GCNN: Augmenting Electroencephalogram-based Neurological Disease Diagnosis using a Domain-guided Graph Convolutional Neural Network"
17:15 - 18:15 Poster Session B
18:15 - 18:30 Social Break
18:30 - 18:50 Andrew Ng: Practical limitations of today's deep learning in healthcare
18:50 - 19:10 Discussion with Andrew Ng
19:10 - 19:20 Closing Remarks with Emily Alsentzer and Matthew McDermott