Chronic Kidney Disease Prognosis Prediction Using Transformer

• URL: http://arxiv.org/abs/2511.02340v1
• Date: Tue, 04 Nov 2025 07:52:17 GMT
• Title: Chronic Kidney Disease Prognosis Prediction Using Transformer
• Authors: Yohan Lee, DongGyun Kang, SeHoon Park, Sa-Yoon Park, Kwangsoo Kim,
• Abstract summary: Chronic Kidney Disease (CKD) affects nearly 10% of the global population and often progresses to end-stage renal failure.<n>We present a transformer-based framework for predicting CKD progression using multi-modal electronic health records.
• Score: 2.054117570146147
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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