  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.
