Population stratification for prediction of mortality in post-AKI patients

Flavio S. Correa da Silva, Simon Sawhney
10/23/2024
cs.LG

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Abstract

Acute kidney injury (AKI) is a serious clinical condition that affects up to 20% of hospitalised patients. AKI is associated with short term unplanned hospital readmission and post-discharge mortality risk. Patient risk and healthcare expenditures can be minimised by followup planning grounded on predictive models and machine learning. Since AKI is multi-factorial, predictive models specialised in different categories of patients can increase accuracy of predictions. In the present article we present some results following this approach.

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Cite this paper

@article{silva2024population,
  title  = {Population stratification for prediction of mortality in post-AKI patients},
  author = {Flavio S. Correa da Silva and Simon Sawhney},
  year   = {2024},
  eprint = {2410.17865},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2410.17865v1}
}

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