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Workshop
Learning from Time Series for Health
Sana Tonekaboni · Thomas Hartvigsen · Satya Narayan Shukla · Gunnar Rätsch · Marzyeh Ghassemi · Anna Goldenberg

@ Physical
Event URL: https://timeseriesforhealth.github.io »

Time series data are ubiquitous in healthcare, from medical time series to wearable data, and present an exciting opportunity for machine learning methods to extract actionable insights about human health. However, huge gap remain between the existing time series literature and what is needed to make machine learning systems practical and deployable for healthcare. This is because learning from time series for health is notoriously challenging: labels are often noisy or missing, data can be multimodal and extremely high dimensional, missing values are pervasive, measurements are irregular, data distributions shift rapidly over time, explaining model outcomes is challenging, and deployed models require careful maintenance over time. These challenges introduce interesting research problems that the community has been actively working on for the last few years, with significant room for contribution still remaining. Learning from time series for health is a uniquely challenging and important area with increasing application. Significant advancements are required to realize the societal benefits of these systems for healthcare. This workshop will bring together machine learning researchers dedicated to advancing the field of time series modeling in healthcare to bring these models closer to deployment.

Author Information

Sana Tonekaboni (University of Toronto / Vector Institute)
Thomas Hartvigsen (Massachusetts Institute of Technology)
Satya Narayan Shukla (University of Massachusetts Amherst)
Gunnar Rätsch (ETH Zürich)
Marzyeh Ghassemi (MIT)
Anna Goldenberg (University of Toronto)

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