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Artificial Intelligence for Acute Respiratory Failure

Edited by:
Prof. Dr. Zhongheng Zhang
Dr. Jakob Wittenstein

Submission Status: Open   |   Submission Deadline: 31.01.2025


Intensive Care Medicine Experimental is calling for submissions to our Collection on Artificial Intelligence for Acute Respiratory Failure. This thematic collection aims to curate a groundbreaking compendium of research that harnesses the power of AI to revolutionize the management and treatment of acute respiratory failure. This collection will not only showcase the cutting-edge advancements in AI applications but also highlight the unique value proposition that these technologies bring to the field of critical care medicine.
 

Image credits: Â© Angelov / Stock.adobe.com

New Content ItemThis collection supports and amplifies research related to SDG 3: Good Health and Well-being

Guest Editors

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Zhang Zhongheng, MD, Zhejiang University, China 

Zhang Zhongheng is an Associate Professor and supervisor at Zhejiang University, China with a robust background in the field of emergency and critical care medicine. He has made significant contributions to the medical field, with a particular focus on sepsis, Acute Respiratory Distress Syndrome (ARDS) and big data analytics. His research interests extend to the application of machine learning and genetic algorithms in healthcare, as well as the use of next-generation sequencing and single-cell sequencing technologies to advance medical research. Dr. Zhang is an active member of several academic societies.

Jakob Wittenstein, MD, University Hospital Dresden, Germany

Jakob Wittenstein is a clinician scientist and anaesthesiologist at the University Hospital Dresden, Germany. He is interested in clinical and translational research in acute respiratory failure. A particular focus of his research is mechanical ventilation. As one of the leaders of the Pulmonary Engineering Group Dresden, he explores innovative mechanical ventilation strategies and addresses a wide range of novel questions in respiratory pathophysiology. He also leads the international IntelliLung consortium, which aims to develop and validate an AI-based decision support system to optimise mechanical ventilation in critically ill patients.

Submission Guidelines

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Before submitting your manuscript, please ensure you have read our submission guidelines. Articles for this Collection should be submitted via our submission system. During the submission process, under the section additional information, you will be asked whether you are submitting to a Collection, please select "Artificial Intelligence for Acute Respiratory Failure" from the dropdown menu.

Articles will undergo the journal’s standard peer-review process and are subject to all of the journal’s standard policies. Articles will be added to the Collection as they are published.

The Guest Editors have no competing interests with the submissions which they handle through the peer review process. The peer review of any submissions for which the Guest Editors have competing interests is handled by another Editorial Board Member who has no competing interests.

  1. Reinforcement learning (RL) holds great promise for intensive care medicine given the abundant availability of data and frequent sequential decision-making. But despite the emergence of promising algorithms, R...

    Authors: Luca F. Roggeveen, Ali el Hassouni, Harm-Jan de Grooth, Armand R. J. Girbes, Mark Hoogendoorn and Paul W. G. Elbers
    Citation: Intensive Care Medicine Experimental 2024 12:32