Workflow of validating the pyAKI pipeline.

Workflow of validating the pyAKI pipeline.

<div><p>Objective</p><p>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 (KDIG...

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Bibliographic Details
Main Author: Christian Porschen (12967868) (author)
Other Authors: Jan Ernsting (5439722) (author), Paul Brauckmann (20501244) (author), Raphael Weiss (15237811) (author), Till Würdemann (20501247) (author), Hendrik Booke (11191483) (author), Wida Amini (20501250) (author), Ludwig Maidowski (16735858) (author), Benjamin Risse (275593) (author), Tim Hahn (341356) (author), Thilo von Groote (13959036) (author)
Published: 2025
Subjects:
Medicine
Genetics
Molecular Biology
Biotechnology
Science Policy
Infectious Diseases
Mathematical Sciences not elsewhere classified
Information Systems not elsewhere classified
medical information mart
introducing different interpretations
improving global outcomes
implement classification algorithms
commonly used database
urinary output data
data scientists working
pipeline &# 8217
work introduces pyaki
pyaki &# 8212
project introduces pyaki
source pipeline addressing
xlink "> validation
intensive care units
critically ill patients
critical care research
consistent aki classification
comparative analysis revealed
time series data
standardized data model
high accuracy make
open source solution
implementing kdigo criteria
xlink ">
time series
pyaki pipeline
intensive care
comparative analysis
source solution
aki data
source tools
high accuracy
resource intensive
research applications
pyaki implements
comprehensive solution
clinical research
valuable tool
serum creatinine
robust performance
requires researchers
kidney disease
kdigo criteria
human labels
frequent complication
first open
ensure reproducibility
edge cases
diagnostic algorithm
diagnostic accuracy
aki diagnosis
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