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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 asserious complications. The prevalence of CKD has been increasing in the lastcouple of decades, which is partly due to the increased prevalence of diabetesand hypertension. To accurately detect CKD in diabetic patients, we propose anovel framework to learn sparse longitudinal representations of patients'medical records. The proposed method is also compared with widely usedbaselines such as Aggregated Frequency Vector and Bag-of-Pattern in Sequenceson real EHR data, and the experimental results indicate that the proposed modelachieves higher predictive performance. Additionally, the learnedrepresentations are interpreted and visualized to bring clinical insights.

Sparse Longitudinal Representations of Electronic Health Record Data for the Early Detection of Chronic Kidney Disease in Diabetic Patients