• Corpus ID: 235794824

Predicting sepsis in multi-site, multi-national intensive care cohorts using deep learning

@article{Moor2021PredictingSI,
  title={Predicting sepsis in multi-site, multi-national intensive care cohorts using deep learning},
  author={Michael Moor and Nicolas Bennet and Drago Ple{\vc}ko and Max Horn and Bastian Rieck and Nicolai Meinshausen and Peter Buhlmann and Karsten M. Borgwardt},
  journal={ArXiv},
  year={2021},
  volume={abs/2107.05230}
}
Despite decades of clinical research, sepsis remains a global public health crisis with high mortality, and morbidity. Currently, when sepsis is detected and the underlying pathogen is identified, organ damage may have already progressed to irreversible stages. Effective sepsis management is therefore highly time-sensitive. By systematically analysing trends in the plethora of clinical data available in the intensive care unit (ICU), an early prediction of sepsis could lead to earlier pathogen… 

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AI Gone Astray: Technical Supplement
TLDR
MIMIC-IV, a publicly available dataset, is used to train models that replicate commercial approaches by Dascena and Epic to predict the onset of sepsis, a deadly and yet treatable condition.