  Healthcare professionals have long envisioned using the enormous processing
powers of computers to discover new facts and medical knowledge locked inside
electronic health records. These vast medical archives contain time-resolved
information about medical visits, tests and procedures, as well as outcomes,
which together form individual patient journeys. By assessing the similarities
among these journeys, it is possible to uncover clusters of common disease
trajectories with shared health outcomes. The assignment of patient journeys to
specific clusters may in turn serve as the basis for personalized outcome
prediction and treatment selection. This procedure is a non-trivial
computational problem, as it requires the comparison of patient data with
multi-dimensional and multi-modal features that are captured at different times
and resolutions. In this review, we provide a comprehensive overview of the
tools and methods that are used in patient similarity analysis with
longitudinal data and discuss its potential for improving clinical decision
making.
