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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التفاصيل البيبلوغرافية
المؤلف الرئيسي:	Christian Porschen (12967868) (author)
مؤلفون آخرون:	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)
منشور في:	2025
الموضوعات:	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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Workflow of validating the pyAKI pipeline.

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