Adapting Animal Models to Assess Sufficiency of Fluid Resuscitation in Humans (Student Abstract)
Abstract
Fluid resuscitation is an initial treatment frequently employed to treat shock, restore lost blood, protect tissues from injury, and prevent organ dysfunction in critically ill patients. However, it is not without risk (e.g., overly aggressive resuscitation may cause organ damage and even death). We leverage machine learning models trained to assess sufficiency of resuscitation in laboratory animals subjected to induced hemorrhage and transfer them to use with human trauma patients. Our key takeaway is that animal experiments and models can inform human healthcare, especially when human data is limited or when collecting relevant human data via potentially harmful protocols is unfeasible.
Cite
Text
Schuerkamp et al. "Adapting Animal Models to Assess Sufficiency of Fluid Resuscitation in Humans (Student Abstract)." AAAI Conference on Artificial Intelligence, 2024. doi:10.1609/AAAI.V38I21.30508Markdown
[Schuerkamp et al. "Adapting Animal Models to Assess Sufficiency of Fluid Resuscitation in Humans (Student Abstract)." AAAI Conference on Artificial Intelligence, 2024.](https://mlanthology.org/aaai/2024/schuerkamp2024aaai-adapting/) doi:10.1609/AAAI.V38I21.30508BibTeX
@inproceedings{schuerkamp2024aaai-adapting,
title = {{Adapting Animal Models to Assess Sufficiency of Fluid Resuscitation in Humans (Student Abstract)}},
author = {Schuerkamp, Ryan and Li, Xinyu and Kunzer, Brian and Weiss, Leonard S. and Gómez, Hernando and Guyette, Francis X. and Pinsky, Michael R. and Dubrawski, Artur},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2024},
pages = {23644-23646},
doi = {10.1609/AAAI.V38I21.30508},
url = {https://mlanthology.org/aaai/2024/schuerkamp2024aaai-adapting/}
}