Research Overview
Led by Drs. Andrew Admon (Internal Medicine) and Christopher Gillies (Emergency Medicine), this team is using Machine Learning, a powerful data science tool, to build a real-time patient surveillance system. During times of unprecedented strain on healthcare personnel and clinical resources, this will help clinicians identify COVID-19 patients who need more intensive monitoring, closer nursing care, or urgent physician intervention.
The MIDAS COVID-19 Propelling Original Data Science (PODS) grants were awarded on May 13th, 2020, each of the 7 teams received funding of up to $30,000 with projects starting immediately and expected to finish by the end of 2020. These projects demonstrate the resolve, expertise, and creativity of U-M data scientists facing a public health crisis. To learn more about the other projects please visit our COVID-19 PODS awards page.
Research Team
Andrew Admon, MD, MSc, Department of Internal Medicine
Christopher E. Gillies, PhD, Department of Emergency Medicine
Sardar Ansari, PhD, Department of Emergency Medicine
Jonathan Motyka, MS, Department of Emergency Medicine
Brandon Cummings, BS (MS Expected 2020), Department of Emergency Medicine
Guan Wang, MS, Department of Emergency Medicine
Research Highlights
Recent Publications
A Multilevel Bayesian Approach to Improve Effect Size Estimation in Regression Modeling of Metabolomics Data Utilizing Imputation with Uncertainty
Updates
This team is in the process of submitting the first paper to a journal related to this project. They have submitted an R01 related to this work.
