Leveraging Pre-trained and Transformer-derived Embeddings from EHRs to
  Characterize Heterogeneity Across Alzheimer's Disease and Related Dementias
Matthew West Colin Magdamo Lily Cheng Yingnan He Sudeshna Das
Alzheimer's disease is a progressive, debilitating neurodegenerative disease
that affects 50 million people globally. Despite this substantial health
burden, available treatments for the disease are limited and its fundamental
causes remain poorly understood. Previous work has suggested the existence of
clinically-meaningful sub-types, which it is suggested may correspond to
distinct etiologies, disease courses, and ultimately appropriate treatments.
Here, we use unsupervised learning techniques on electronic health records
(EHRs) from a cohort of memory disorder patients to characterise heterogeneity
in this disease population. Pre-trained embeddings for medical codes as well as
transformer-derived Clinical BERT embeddings of free text are used to encode
patient EHRs. We identify the existence of sub-populations on the basis of
comorbidities and shared textual features, and discuss their clinical
significance.