pyAKI—An open source solution to automated acute kidney injury classification

Porschen, Christian and Ernsting, Jan and Brauckmann, Paul and Weiss, Raphael and Würdemann, Till and Booke, Hendrik and Amini, Wida and Maidowski, Ludwig and Risse, Benjamin and Hahn, Tim and von Groote, Thilo

Research article (journal) | Peer reviewed

Details about the publication

Journal: PloS one (PLoS One)

Volume: 20

Issue: 1

Article number: e0315325

Status: Published

Release year: 2025

Language in which the publication is written: English

Link to the full text: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0315325

Keywords: acute kidney injury; machine learning; classification; open source

Authors from the University of Münster

Amini, Wida	Clinic for Anaesthesiology, Surgical Critical Care Medicine and Pain Therapy
Ernsting, Jan	Professorship of Geoinformatics for Sustainable Development (Prof. Risse)
Groote, Thilo Caspar	Clinic for Anaesthesiology, Surgical Critical Care Medicine and Pain Therapy
Hahn, Tim	Institute of Translational Psychiatry
Porschen, Christian	Clinic for Anaesthesiology, Surgical Critical Care Medicine and Pain Therapy
Risse, Benjamin	Professorship of Geoinformatics for Sustainable Development (Prof. Risse)
Weiss, Raphael	Clinic for Anaesthesiology, Surgical Critical Care Medicine and Pain Therapy
Würdemann, Till Janusz	Clinic for Anaesthesiology, Surgical Critical Care Medicine and Pain Therapy

pyAKI—An open source solution to automated acute kidney injury classification

Details about the publication

Authors from the University of Münster

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