A deep learning backcasting approach to the electrolyte, metabolite, and acid-base parameters that predict risk in ICU patients

Albion Dervishi

doi:10.1371/journal.pone.0242878

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Open Access

Peer-reviewed

Research Article

A deep learning backcasting approach to the electrolyte, metabolite, and acid-base parameters that predict risk in ICU patients

Albion Dervishi

Roles Conceptualization, Formal analysis, Funding acquisition, Investigation, Methodology, Validation, Visualization, Writing – original draft

* E-mail: albiondervishi@gmail.com

Affiliation Department of Anesthesiology and Intensive Care Medicine, Medius Clinic Nürtingen, Academic Teaching Hospital of the University of Tübingen, Tübingen, Germany

https://orcid.org/0000-0001-7140-836X

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A deep learning backcasting approach to the electrolyte, metabolite, and acid-base parameters that predict risk in ICU patients

Albion Dervishi

Published: December 17, 2020
https://doi.org/10.1371/journal.pone.0242878

About the Authors

Albion Dervishi

Roles Conceptualization, Formal analysis, Funding acquisition, Investigation, Methodology, Validation, Visualization, Writing – original draft

* E-mail: albiondervishi@gmail.com

Affiliation Department of Anesthesiology and Intensive Care Medicine, Medius Clinic Nürtingen, Academic Teaching Hospital of the University of Tübingen, Tübingen, Germany

https://orcid.org/0000-0001-7140-836X

Competing Interests

The authors have declared that no competing interests exist.

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Electrolytes
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Medical risk factors
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Metabolites
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Bicarbonates
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Intensive care units
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Chlorides
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Glucose
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Physiological parameters
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