  A meaningful understanding of clinical protocols and patient pathways helps
improve healthcare outcomes. Electronic health records (EHR) reflect real-world
treatment behaviours that are used to enhance healthcare management but present
challenges; protocols and pathways are often loosely defined and with elements
frequently not recorded in EHRs, complicating the enhancement. To solve this
challenge, healthcare objectives associated with healthcare management
activities can be indirectly observed in EHRs as latent topics. Topic models,
such as Latent Dirichlet Allocation (LDA), are used to identify latent patterns
in EHR data. However, they do not examine the ordered nature of EHR sequences,
nor do they appraise individual events in isolation. Our novel approach, the
Categorical Sequence Encoder (CaSE) addresses these shortcomings. The
sequential nature of EHRs is captured by CaSE's event-level representations,
revealing latent healthcare objectives. In synthetic EHR sequences, CaSE
outperforms LDA by up to 37% at identifying healthcare objectives. In the
real-world MIMIC-III dataset, CaSE identifies meaningful representations that
could critically enhance protocol and pathway development.
