  Dementia is characterized by a decline in memory and thinking that is
significant enough to impair function in activities of daily living. Patients
seen in dementia specialty clinics are highly heterogeneous with a variety of
different symptoms that progress at different rates. In this work, we used an
unsupervised data-driven K-Means clustering approach on the component scores of
the Clinical Dementia Rating (CDR) score to identify dementia subtypes and used
the gap-statistic to identify the optimal number of clusters. Our goal was to
characterize the identified dementia subtypes in terms of their cognitive
performance and analyze how patient transitions between subtypes relate to
disease progression. Our results indicate both inter-subtype variability, which
indicates the variability amongst dementia subtypes for a particular component
score even with the same CDR and (ii) intra-subtype variability, which
indicates the variation in the 6 component scores within a particular dementia
subtype. We observed that dementia subtypes that represented individuals with
very mild dementia (CDR 0.5) had widely varying rates of transition to other
subtypes. Future work includes testing the generalizability of our proposed
pipeline on additional datasets, and using a larger volume of EHR data to
estimate probabilistic estimates of the variability between dementia subtypes
both in terms of cognitive profile and disease progression.
