论文标题
电子健康记录数据的稀疏纵向表示，用于早期检测糖尿病患者的慢性肾脏疾病
Sparse Longitudinal Representations of Electronic Health Record Data for the Early Detection of Chronic Kidney Disease in Diabetic Patients
论文作者
论文摘要
慢性肾脏疾病（CKD）是随着时间的流逝逐渐丧失肾功能，它增加了死亡率，降低生活质量以及严重并发症的风险。在过去的几十年中，CKD的患病率一直在增加，这部分是由于糖尿病和高血压的患病率增加。为了准确检测糖尿病患者的CKD，我们提出了一个新的框架，以学习患者病历的稀疏纵向表现。还将所提出的方法与广泛使用的基线（例如实际EHR数据序列中的汇总频率向量和pattern）进行了比较，实验结果表明，所提出的模型可实现更高的预测性能。此外，对学习的表示形式进行了解释和可视化，以带来临床见解。
Chronic kidney disease (CKD) is a gradual loss of renal function over time, and it increases the risk of mortality, decreased quality of life, as well as serious complications. The prevalence of CKD has been increasing in the last couple of decades, which is partly due to the increased prevalence of diabetes and hypertension. To accurately detect CKD in diabetic patients, we propose a novel framework to learn sparse longitudinal representations of patients' medical records. The proposed method is also compared with widely used baselines such as Aggregated Frequency Vector and Bag-of-Pattern in Sequences on real EHR data, and the experimental results indicate that the proposed model achieves higher predictive performance. Additionally, the learned representations are interpreted and visualized to bring clinical insights.
