用Transformer预测慢性肾病进展，准确率超99%。
Chronic Kidney Disease Prognosis Prediction Using Transformer
- 将连续检验值量化为令牌，结合注意力机制建模多模态医疗数据。
- 在9万1千余名患者上实现0.995的ROC-AUC与0.989的PR-AUC。
- 适合临床预后研究与个性化肾病管理，可解释性强。
慢性肾病（CKD）影响全球近10%的人口，常进展至终末期肾衰竭。准确预测预后对及时干预和资源优化至关重要。我们提出基于Transformer的框架ProQ-BERT，利用首尔国立大学医院OMOP通用数据模型中的多模态电子健康记录（EHR）预测CKD进展。该方法整合了人口统计、临床及实验室数据，采用基于量化的分词方式处理连续检验值，并通过注意力机制提升可解释性。模型通过掩码语言建模预训练，再针对从3a期到5期的二分类任务微调，评估涵盖不同随访与评估周期。在91,816名患者的队列中，其性能持续优于CEHR-BERT，短期预测的ROC-AUC最高达0.995，PR-AUC最高达0.989。结果表明，Transformer架构与时间设计在临床预后建模中具有显著优势，为个性化CKD管理提供了新方向。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。