  Cardiovascular disorders account for nearly 1 in 3 deaths in the United
States. Care for these disorders are often determined during visits to acute
care facilities, such as hospitals. While the length of stay in these settings
represents just a small proportion of patients' lives, they account for a
disproportionately large amount of decision making. To overcome this bias
towards data from acute care settings, there is a need for longitudinal
monitoring in patients with cardiovascular disorders. Longitudinal monitoring
can provide a more comprehensive picture of patient health, allowing for more
informed decision making. This work surveys the current field of sensing
technologies and machine learning analytics that exist in the field of remote
monitoring for cardiovascular disorders. We highlight three primary needs in
the design of new smart health technologies: 1) the need for sensing technology
that can track longitudinal trends in signs and symptoms of the cardiovascular
disorder despite potentially infrequent, noisy, or missing data measurements;
2) the need for new analytic techniques that model data captured in a
longitudinal, continual fashion to aid in the development of new risk
prediction techniques and in tracking disease progression; and 3) the need for
machine learning techniques that are personalized and interpretable, allowing
for advancements in shared clinical decision making. We highlight these needs
based upon the current state-of-the-art in smart health technologies and
analytics and discuss the ample opportunities that exist in addressing all
three needs in the development of smart health technologies and analytics
applied to the field of cardiovascular disorders and care.
