# Population stratification for prediction of mortality in post-AKI patients

arXiv: https://arxiv.org/abs/2410.17865 · HTML: https://mlsanity.com/paper/2410.17865/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 33 references that are in the map.
- First posted on arXiv 23 Oct 2024.

## Builds on (most cited first, 1 of 1)

- [Evaluating Patient Readmission Risk: A Predictive Analytics Approach](https://mlsanity.com/paper/1812.11028/) (2018)

## Abstract (arXiv)

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.