{"ID":2846107,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.02340","arxiv_id":"2511.02340","title":"Chronic Kidney Disease Prognosis Prediction Using Transformer","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.","short_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 healt...","url_abs":"https://arxiv.org/abs/2511.02340","url_pdf":"https://arxiv.org/pdf/2511.02340v2","authors":"[\"Yohan Lee\",\"DongGyun Kang\",\"SeHoon Park\",\"Sa-Yoon Park\",\"Kwangsoo Kim\"]","published":"2025-11-04T07:52:17Z","proceeding":"cs.AI","tasks":"[\"cs.AI\",\"q-bio.OT\"]","methods":"[\"Transformer\",\"Language Model\"]","has_code":false}