Predicting Nephrotoxic Acute Kidney Injury in Hospitalized Adults: A Machine Learning Algorithm
Our RNN-GRU model substantially reduced the number of false alerts for nephrotoxic AKI, which may facilitate NINJA translation to adult hospitals by providing more targeted intervention.
Authors
- Michael E. Matheny (ORCID: https://orcid.org/0000-0003-3217-4147)
- Stuart L. Goldstein (ORCID: https://orcid.org/0000-0003-3711-8785)
- Mary Vaughan‐Sarrazin (ORCID: https://orcid.org/0000-0001-8717-1061)
- Michihiko Goto (ORCID: https://orcid.org/0000-0001-6612-5613)
- Jason Misurac (ORCID: https://orcid.org/0000-0002-6800-7650)
- W. Nick Street (ORCID: https://orcid.org/0000-0002-1632-5905)
- Benjamin R. Griffin (ORCID: https://orcid.org/0000-0001-8599-5462)
- Diana I. Jalal (ORCID: https://orcid.org/0000-0002-1975-8650)
- Avinash Reddy Mudireddy (ORCID: https://orcid.org/0000-0002-5252-6990)
- Benjamin D. Horne (ORCID: https://orcid.org/0000-0002-4674-4553)
- Michel Chonchol
Institutions
- Intermountain Healthcare (US)
- University of Iowa (US)
- Cincinnati Children's Hospital Medical Center (US)
- Intermountain Medical Center (US)
- VA Tennessee Valley Healthcare System (US)
- University of Iowa Stead Family Children’s Hospital (US)
- Iowa City VA Health Care System (US)
- Stanford Medicine (US)
- University of Colorado Anschutz Medical Campus (US)
- Vanderbilt University Medical Center (US)
Publication Details
- Journal
- Kidney Medicine
- Published
- 2024-10-16
- DOI
- https://doi.org/10.1016/j.xkme.2024.100918
- Citations
- 5
- Primary Topic
- Acute Kidney Injury Research
- Type
- article
- Field-Weighted Citation Impact
- 1.70