Artificial Intelligence and Data Science Transforming Kidney Care: From Algorithms to Action
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Moderator(s): Karin Bergling, Wisit Cheungpasitporn

Presentation(s):
  • Temporal Machine-Learning Model for Predicting Seven-Day RRT-Free Survival in Critically Ill Patients on Continuous Renal Replacement Therapy - Joanna Yang
  • Machine Learning-Driven Digital Phenotyping of Accelerometer-Based Physical Activity Patterns and Mortality in Patients on Hemodialysis: The PROMOTE Study - Hikaru Kadono
  • Real-World Validation and Optimization of CKD Progression Prediction Models Using US Mayo Clinic Data - David Chang
  • Integrating Pathomic and Clinical Data Using Artificial Intelligence (AI) Models from Kidney Deceased Donors to Reduce Organ Discard and Improve Transplant Outcomes - Luis Rodrigues
  • NephroAIX: Large Language Model (LLM)-Guided Knowledge Graph for Explainable Target Discovery in Human Kidney Diseases Using Single-Cell Data - Sikander Hayat
  • TRIMS: Deep-Learning Segmentation of Renal Tubules and Immune Cells in 32-Marker Spatial Proteomic Images from Lupus Nephritis Biopsies - Mahith Gottipati
  • Large-Scale Systemic Proteome Profiling of More Than 5,000 Proteins to Differentiate Primary Glomerular Diseases - Jae-ik Oh
  • High-Throughput Nephron Tracking Reveals Hierarchical Organization in Mouse Kidneys - Joshua Vaughan
  • Human Induced Pluripotent Stem Cell (iPSC)-Derived Glomerulus-on-a-Chip for Disease Modeling and Drug Testing - Laura Locatelli

Note: Continuing education credits are not being offered for this session.
Meta Tag
Date 11/7/2025
Session ID 519818
Keywords
artificial intelligence in nephrology
data science kidney care
CRRT ICU prediction
XGBoost temporal model
TSFRESH EHR feature extraction
SHAP interpretability
hemodialysis accelerometer phenotyping
UMAP k-means clustering
CKD progression GRU model
kidney transplant decision support
computational pathology Remuzzi scoring
plasma proteomics glomerular disease classification
glomerulus-on-a-chip iPSC modeling