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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

Application of a machine learning risk prediction model predicts chronic kidney disease in sickle cell disease patients, indicating novel insights into healthcare utilization.

Key Points

  • This research aims to predict chronic kidney disease using a machine learning model for patients with sickle cell disease.
  • Applied a validated phenotyping algorithm for chronic kidney disease
  • Analyzed data from 13,284 sickle cell disease patients in a multi-site electronic health record database
  • Trained random forest classifiers to predict chronic kidney disease progression using 31 clinical features
  • Employed Shapley Additive exPlanations for interpretability of predictive factors
  • Identified 2,767 chronic kidney disease cases among sickle cell patients
  • Achieved 85% accuracy in predicting chronic kidney disease
  • Notable predictive features included age, sex, and healthcare utilization metrics
  • Highlighted the importance of lab measurements in CKD risk assessment

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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