Improved real-time surveillance of COVID-19 patients’ electronic health records using transfer learning and ordinal regression

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 Impact

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