  Sequential diagnosis prediction on the Electronic Health Record (EHR) has
been proven crucial for predictive analytics in the medical domain. EHR data,
sequential records of a patient's interactions with healthcare systems, has
numerous inherent characteristics of temporality, irregularity and data
insufficiency. Some recent works train healthcare predictive models by making
use of sequential information in EHR data, but they are vulnerable to
irregular, temporal EHR data with the states of admission/discharge from
hospital, and insufficient data. To mitigate this, we propose an end-to-end
robust transformer-based model called SETOR, which exploits neural ordinary
differential equation to handle both irregular intervals between a patient's
visits with admitted timestamps and length of stay in each visit, to alleviate
the limitation of insufficient data by integrating medical ontology, and to
capture the dependencies between the patient's visits by employing multi-layer
transformer blocks. Experiments conducted on two real-world healthcare datasets
show that, our sequential diagnoses prediction model SETOR not only achieves
better predictive results than previous state-of-the-art approaches,
irrespective of sufficient or insufficient training data, but also derives more
interpretable embeddings of medical codes. The experimental codes are available
at the GitHub repository (https://github.com/Xueping/SETOR).
