Press enter after choosing selection

Friday Night AI | Faster than COVID: Can AI Predict a Disease's Next Move?


Friday April 8, 2022: 7:00pm to 8:00pm  Add to Calendar /   Add to Google Calendar


Zoom & AADL.TV


Over the past two years, we have witnessed how the spread of a virus can result in a world-wide pandemic, and how rapid mutations of the virus can lead to periodic spikes and challenge even the most advanced healthcare systems. While hopes are now high that the pandemic will slow down, what have we learned about how AI can help us predict a disease’s next move?

Join us to learn more about a computational model now in use at Michigan Medicine that can help clinicians anticipate fast-changing patient needs. Called M-CURES and developed by a team of computer science, industrial operations and engineering and health care researchers, the model uses a machine-learning algorithm to predict which patients are at greatest risk of developing severe illness. Internal and external validation of M-CURES has shown it to be effective in predicting the progression of the disease across 12+ hospital centers and patient subgroups.

Speakers: Prof. Jenna Wiens and Prof. Karandeep Singh

Jenna Wiens is an Associate Professor of Computer Science and Engineering (CSE), Associate Director of the AI Lab, and co-Director of Precision Health at the University of Michigan in Ann Arbor. Her primary research interests lie at the intersection of machine learning and healthcare. Wiens received her PhD from MIT in 2014, was named to the MIT Tech Review’s list of Innovators Under 35 in 2017, and recently was awarded a Sloan Research Fellowship in Computer Science.

Karandeep Singh is an Assistant Professor of Learning Health Sciences, Internal Medicine, Urology, and Information at the University of Michigan. He directs the Machine Learning for Learning Health Systems (ML4LHS) Lab. His recent work focuses on studying the implementation of machine learning algorithms within health systems.


Friday Night AI Faster than Covid. Can AI predict disease?

This event was held live on Zoom and has concluded.


Hi there! Here is the answer from our presenters:

Directly from the paper: The final nine variables included age, respiratory rate, oxygen saturation, oxygen flow rate, pulse oximetry type (eg, continuous, intermittent), head-of-bed position (eg, at 30°), position of patient during blood pressure measurement (standing, sitting, lying), venous blood gas pH, and partial pressure of carbon dioxide in arterial blood.
Here's the link to the paper:

Karandeep Singh, MD, MMSc