  Subtyping of Alzheimer's disease (AD) can facilitate diagnosis, treatment,
prognosis and disease management. It can also support the testing of new
prevention and treatment strategies through clinical trials. In this study, we
employed spectral clustering to cluster 29,922 AD patients in the OneFlorida
Data Trust using their longitudinal EHR data of diagnosis and conditions into
four subtypes. These subtypes exhibit different patterns of progression of
other conditions prior to the first AD diagnosis. In addition, according to the
results of various statistical tests, these subtypes are also significantly
different with respect to demographics, mortality, and prescription medications
after the AD diagnosis. This study could potentially facilitate early detection
and personalized treatment of AD as well as data-driven generalizability
assessment of clinical trials for AD.
