Department of Health Informatics and Data Science and 

Center for Health Outcome and Informatics Research



“Artificial Intelligence Can Predict the Risk of ARDS, ICU Admission, and Mortality in Patients Presenting to Emergency Department During the COVID-19 Pandemic”


  Presented by:

Liam Butler, PhD

Research Associate, Department of Health Informatics and Data Sciene, Loyola University Chicago


Abstract: The novel SARS-CoV-2 (COVID-19) has quickly spread globally and was classified as a world pandemic and has substantially increased the influx of people admitted to the emergency department (ED). COVID-19 infection has been directly related to development of acute respiratory distress syndrome (ARDS) and severe infections lead to admission to intensive care and can also lead to death. Dr. Butler will present a CHOIR-funded project that demonstrated the clinical data available at time of admission to ED can be used in machine learning models to assess possible risk of ARDS, need for ICU admission as well as risk of mortality. In addition, chest radiographs can be inputted into deep learning models to further assess the development of ARDS, need of ICU admission and risk of death.


When: Wednesday, June 23rd        11:00 am – 12:00 pm

Join via Zoom: https://luc.zoom.us/j/88067078491

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About the Speaker: Dr. Butler received his Ph.D. in Biology in 2019 from Newcastle University, UK. His background has primarily been in using different statistical, modelling and artificial intelligence techniques to address numerous biological questions ranging from ecology to public health and health informatics. He is a Postdoctoral Research Associate at Loyola University Chicago in Dr. Oguz Akbilgic’s lab. He has been working on different projects using machine learning and deep learning techniques to predict and assess health outcomes including stroke, heart failure and more recently COVID-19 infections and the development of acute respiratory distress syndrome.

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