Population stratification for prediction of mortality in post-AKI patients

Flavio S. Correa da Silva; Simon Sawhney

Population stratification for prediction of mortality in post-AKI patients

Machine Learning 2024-10-24 v1

Authors: Flavio S. Correa da Silva , Simon Sawhney

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.

Keywords

acute kidney injury mortality prediction population stratification machine learning post-discharge risk patient subgroups

Cite

@article{arxiv.2410.17865,
  title  = {Population stratification for prediction of mortality in post-AKI patients},
  author = {Flavio S. Correa da Silva and Simon Sawhney},
  journal= {arXiv preprint arXiv:2410.17865},
  year   = {2024}
}

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