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Chronic kidney disease (CKD) has a poor prognosis due to excessive riskfactors and comorbidities associated with it. The early detection of CKD faceschallenges of insufficient medical histories of positive patients andcomplicated risk factors. In this paper, we propose the TRACE (Transformer-RNNAutoencoder-enhanced CKD Detector) framework, an end-to-end prediction modelusing patients' medical history data, to deal with these challenges. TRACEpresents a comprehensive medical history representation with a novel keycomponent: a Transformer-RNN autoencoder. The autoencoder jointly learns amedical concept embedding via Transformer for each hospital visit, and a latentrepresentation which summarizes a patient's medical history across all thevisits. We compared TRACE with multiple state-of-the-art methods on a datasetderived from real-world medical records. Our model has achieved 0.5708 AUPRCwith a 2.31% relative improvement over the best-performing method. We alsovalidated the clinical meaning of the learned embeddings through visualizationsand a case study, showing the potential of TRACE to serve as a general diseaseprediction model.

TRACE: Early Detection of Chronic Kidney Disease Onset with Transformer-Enhanced Feature Embedding