Chronic Kidney Disease Prognosis Prediction Using Transformer

[Submitted on 4 Nov 2025 (v1), last revised 26 Jun 2026 (this version, v3)]

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Abstract:Chronic Kidney Disease (CKD) affects nearly 10\% of the global population and often progresses to end-stage renal failure. Accurate prognosis prediction is vital for timely interventions and resource optimization. We present a transformer-based framework for predicting CKD progression using multi-modal electronic health records (EHR) from the Seoul National University Hospital OMOP Common Data Model. Our approach (\textbf{ProQ-BERT}) integrates demographic, clinical, and laboratory data, employing quantization-based tokenization for continuous lab values and attention mechanisms for interpretability. The model was pretrained with masked language modeling and fine-tuned for binary classification tasks predicting progression from stage 3a to stage 5 across varying follow-up and assessment periods. Evaluated on a cohort of 91,816 patients, our model consistently outperformed CEHR-BERT, achieving ROC-AUC up to 0.995 and PR-AUC up to 0.989 for short-term prediction. These results highlight the effectiveness of transformer architectures and temporal design choices in clinical prognosis modeling, offering a promising direction for personalized CKD care.

Submission history

From: Dong Gyun Kang Dr. [view email]
[v1] Tue, 4 Nov 2025 07:52:17 UTC (546 KB)
[v2] Tue, 18 Nov 2025 01:31:17 UTC (479 KB)
[v3] Fri, 26 Jun 2026 06:15:12 UTC (479 KB)