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 reviewedDetails 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 |