AI is an important tool to support public health experts around the world in their efforts to keep people safe and informed amid the coronavirus pandemic. Facebook AI is partnering with academic researchers and other experts on a range of initiatives related to COVID-19. We are sharing overviews of several of these now, and we will add updates and more information in the days and weeks to come.
More information is available here about Facebook’s broader efforts related to this pandemic.
Improved COVID-19 forecasting and tools for resource planning
Facebook AI has partnered with New York University’s Courant Institute of Mathematical Sciences to create localized forecasting models of the spread of COVID-19. These local predictions can help health-care providers and emergency responders in a specific county determine how best to allocate their resources (for example, deciding when to adjust a clinic’s staffing schedule to prepare for an expected increase in patients). It is challenging to create forecasts at the county level because the patterns in the data are complex and rapidly evolving. But AI is well suited for this challenge. Facebook AI researchers are using publicly available data published by the State of New Jersey and applying Multivariate Hawkes Processes to create daily COVID-19 predictions for the state. Our colleagues at NYU leverage this information in their models to estimate how progression of the disease will affect hospitals, bed and ICU capacity, and local demand for ventilators, masks, and other PPE needs at a hospital and county level. This information is collectively being shared on a daily basis with the State of New Jersey. Similarly, we have started a collaboration with Cornell University using public data published by the State of New York to model the predicted spread of coronavirus in New York, and we are working with other academic experts to scale these techniques.
We are also collaborating with NYU Langone Health’s Predictive Analytics Unit and Department of Radiology to build hospital-specific forecasts for COVID-19, using reinforcement learning, causal modeling, and supervised/self-supervised learning techniques. These models, which learn from de-identified X-rays and CT scans, as well as other de-identified and aggregated clinical data shared with Facebook in accordance with HIPAA, will help experts better allocate resources for clinical needs and optimize workflow across local hospital systems. For example, using these models, they can predict the number of patients whose condition is likely to improve or worsen in a given time period; how many people are likely to be admitted, transferred to ICUs, or discharged; and the number of ventilators, types of tests, and treatments that might be needed. Facebook AI is neither making nor recommending diagnoses for individual patients.
Similarly, we are partnering with the Mila research institute in Montreal to share predictive, causal, and decision algorithms for analyzing clinical data. No data is being shared in this collaboration, but the project will enable Mila to help hospitals in Montreal use their own patient data to better forecast what resources they will need to treat people with COVID-19.
With these joint efforts with NYU Langone and Mila, our immediate focus is on developing models that can learn from de-identified clinical data and help hospitals determine how to use their resources most effectively. As we refine and build on these techniques, we would like to explore ways to quickly scale the benefits to other organizations. This could include open-sourcing code so that other institutions can train models on their own data.
It’s crucial that public health experts understand the spread of the coronavirus and how best to deploy their resources to help people with COVID-19. We are building on the work described here and looking for more ways to use AI to help address this global crisis.
Source: Facebook AI
