ORAKLE: Optimal Risk prediction for mAke30 in patients with sepsis associated AKI using deep LEarning
ORAKLE is a robust deep-learning model for predicting MAKE30 in critically ill patients with AKI that utilizes evolving time series data. By incorporating dynamically changing time series features, the model captures the evolving nature of kidney injury, treatment effects, and patient trajectories more accurately. This innovation facilitates tailored risk assessments and identifies varying treatment responses, laying the groundwork for more personalized and effective management approaches.
Authors
- Hernando Gómez (ORCID: https://orcid.org/0000-0001-5605-3513)
- Pulkit Agrawal (ORCID: https://orcid.org/0000-0002-9569-6690)
- Ashwin Sawant (ORCID: https://orcid.org/0000-0003-1525-8541)
- John A. Kellum (ORCID: https://orcid.org/0000-0003-1995-2653)
- Won-Suk Oh (ORCID: https://orcid.org/0000-0003-0874-5026)
- Girish N. Nadkarni (ORCID: https://orcid.org/0000-0001-6319-4314)
- Mayte Suárez‐Fariñas (ORCID: https://orcid.org/0000-0001-8712-3553)
- Roopa Kohli‐Seth (ORCID: https://orcid.org/0000-0002-6082-1359)
- KIANOUSH B KASHANI (ORCID: https://orcid.org/0000-0003-2184-3683)
- John Oropello (ORCID: https://orcid.org/0000-0003-4467-3584)
- Ankit Sakhuja
- Marinela Veshtaj
Institutions
- Mayo Clinic (US)
- University of Pittsburgh (US)
- Touro College (US)
- Mount Sinai Hospital (US)
- Massachusetts Institute of Technology (US)
- Icahn School of Medicine at Mount Sinai (US)
Publication Details
- Journal
- Critical Care
- Published
- 2025-05-26
- DOI
- https://doi.org/10.1186/s13054-025-05457-w
- Citations
- 4
- Primary Topic
- Sepsis Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 5.42
Funders
- National Institutes of Health
- National Institute of Diabetes and Digestive and Kidney Diseases