  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 data has a negative impact on
workload and study quality. This project introduces pyAKI, an open-source
pipeline addressing this gap by providing a comprehensive solution for
consistent KDIGO criteria implementation. The pyAKI pipeline was developed and
validated using a subset of the Medical Information Mart for Intensive Care
(MIMIC)-IV database, a commonly used database in critical care research. We
defined a standardized data model in order to ensure reproducibility.
Validation against expert annotations demonstrated pyAKI's robust performance
in implementing KDIGO criteria. Comparative analysis revealed its ability to
surpass the quality of human labels. This work introduces pyAKI as an
open-source solution for implementing the KDIGO criteria for AKI diagnosis
using time series data with high accuracy and performance.
