Artifical Intelligence Can Predict the Risk of ARDS, ICU Admission, and Mortality
Overview:
Liam Butler, PhD, presents a CHOIR-funded project that demonstrated the clinical data available at time of admission to the emergency department 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.
Speaker:
Liam Butler received his PhD 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 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.
Watch previous presentations and to find more information about future seminars.
Originally recorded on Wednesday, June 23, 2021, as part of CHOIR's
Overview:
Liam Butler, PhD, presents a CHOIR-funded project that demonstrated the clinical data available at time of admission to the emergency department 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.
Speaker:
Liam Butler received his PhD 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 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.
Watch previous presentations and to find more information about future seminars.