PyAKI-An open source solution to automated acute kidney injury classification.

ABSTRACT:

Objective

Acute kidney injury (AKI) is a frequent complication in critically ill patients, affecting up to 50% of patients in the intensive care units. The lack of standardized and open-source tools for applying the Kidney Disease Improving Global Outcomes (KDIGO) criteria to time series, requires researchers to implement classification algorithms of their own which is resource intensive and might impact study quality by introducing different interpretations of edge cases. This project introduces pyAKI, an open-source pipeline addressing this gap by providing a comprehensive solution for consistent KDIGO criteria implementation.

Materials and methods

The pyAKI pipeline was developed and validated using a subset of the Medical Information Mart for Intensive Care (MIMIC)-IV da

SUBMITTER: Porschen C 

PROVIDER: S-EPMC11698361 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature