  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.
