论文标题
痕迹：具有变压器增强功能嵌入的慢性肾脏疾病发作的早期检测
TRACE: Early Detection of Chronic Kidney Disease Onset with Transformer-Enhanced Feature Embedding
论文作者
论文摘要
慢性肾脏疾病（CKD）由于与之相关的过量危险因素和合并症，预后不佳。 CKD的早期发现面临阳性患者病史和复杂危险因素的病史不足的挑战。在本文中，我们提出了使用患者的病史数据的痕量（变形金刚RNN自动编码器增强CKD检测器）框架，以应对这些挑战。 Trace具有新颖的关键组件：变压器RNN自动编码器。自动编码器共同学习通过变压器进行每次医院访问的医疗概念，以及一个潜在的代表，总结了所有访问中患者的病史。我们将跟踪与从实际病历得出的数据集中的多种最新方法进行了比较。我们的模型已达到0.5708 AUPRC，比表现最佳的方法相对改善了2.31％。我们还通过可视化和案例研究验证了学到的嵌入的临床含义，显示了痕量作为一般疾病预测模型的潜力。
Chronic kidney disease (CKD) has a poor prognosis due to excessive risk factors and comorbidities associated with it. The early detection of CKD faces challenges of insufficient medical histories of positive patients and complicated risk factors. In this paper, we propose the TRACE (Transformer-RNN Autoencoder-enhanced CKD Detector) framework, an end-to-end prediction model using patients' medical history data, to deal with these challenges. TRACE presents a comprehensive medical history representation with a novel key component: a Transformer-RNN autoencoder. The autoencoder jointly learns a medical concept embedding via Transformer for each hospital visit, and a latent representation which summarizes a patient's medical history across all the visits. We compared TRACE with multiple state-of-the-art methods on a dataset derived from real-world medical records. Our model has achieved 0.5708 AUPRC with a 2.31% relative improvement over the best-performing method. We also validated the clinical meaning of the learned embeddings through visualizations and a case study, showing the potential of TRACE to serve as a general disease prediction model.
