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December 8, 2025BloodOpen Access

Explainable AI-based prediction of chronic kidney disease as a long-term outcome of sickle cell disease in a large, multi-site observational data cohort

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Authors

BABiree AndemariamKTK. Araujo Torres

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Overview

Observational analysis predicts chronic kidney disease in sickle cell disease patients, suggesting the role of healthcare utilization metrics.

Key Points

  • Predictive model achieved 85% accuracy in identifying chronic kidney disease among sickle cell disease patients.
  • F1-score of 0.80 and AUROC of 0.93 show strong performance of the model for chronic kidney disease.
  • Random forest classifiers utilized to analyze electronic health record data from 13,284 verified sickle cell disease patients.
  • High-ranking features included healthcare utilization metrics, emphasizing the importance of clinical interactions.

Cite This Study

Andemariam et al. (2025) studied this question.

synapsesocial.com/papers/69362f514fa91c937236d9a1https://doi.org/10.1182/blood-2025-2967
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Also Consider

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  3. 3Machine learning replicates and extends clinician-informed disease severity grading classification for acute pain and CKD in sickle cell disease2025
  4. 4Bayesian Optimization Meets Explainable AI: Enhanced Chronic Kidney Disease Risk Assessment2025 · 6 citations
  5. 5Evaluation of a Chronic Kidney Disease e‐Phenotype: Identification and Characterisation by Electronic Health Record Data2026