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Machine Learning-Based Algorithm to Predict Death from COVID-19 episode artwork

EPISODE · Feb 23, 2021 · 17 MIN

Machine Learning-Based Algorithm to Predict Death from COVID-19

from Springer Nature · host Springer Nature

Dr. Netto discusses with his two guests, Dr. Peter MCaffrey from the UTMB in Galveston and Dr. Adam Booth, their publication on developing a machine learning model using 5 serum chemistry laboratory parameters for the prediction of death from COVID-19. The discussion is followed by a conversation on the exciting role of AI and social media in Pathology given the unique expertise of the guests. Associated article: Development of a prognostic model for mortality in COVID-19 infection using machine learning https://www.nature.com/articles/s41379-020-00700-x

What this episode covers

Dr. Netto discusses with his two guests, Dr. Peter MCaffrey from the UTMB in Galveston and Dr. Adam Booth, their publication on developing a machine learning model using 5 serum chemistry laboratory parameters for the prediction of death from COVID-19. The discussion is followed by a conversation on the exciting role of AI and social media in Pathology given the unique expertise of the guests. Associated article: Development of a prognostic model for mortality in COVID-19 infection using machine learning https://www.nature.com/articles/s41379-020-00700-x

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Machine Learning-Based Algorithm to Predict Death from COVID-19

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How long is this episode of Springer Nature?

This episode is 17 minutes long.

When was this Springer Nature episode published?

This episode was published on February 23, 2021.

What is this episode about?

Dr. Netto discusses with his two guests, Dr. Peter MCaffrey from the UTMB in Galveston and Dr. Adam Booth, their publication on developing a machine learning model using 5 serum chemistry laboratory parameters for the prediction of death from...

Can I download this Springer Nature episode?

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