A key prerequisite for precision medicine is the estimation of disease
progression from the current patient state. Disease correlations and
temporal disease progression (trajectories) have mainly been analysed
with focus on a small number of diseases or using large-scale approaches
without time consideration, exceeding a few years. So far, no
large-scale studies have focused on defining a comprehensive set of
disease trajectories. Here we present a discovery-driven analysis of
temporal disease progression patterns using data from an electronic
health registry covering the whole population of Denmark. We use the
entire spectrum of diseases and convert 14.9 years of registry data on
6.2 million patients into 1,171 significant trajectories. We group these
into patterns centred on a small number of key diagnoses such as
chronic obstructive pulmonary disease (COPD) and gout, which are central
to disease progression and hence important to diagnose early to
mitigate the risk of adverse outcomes. We suggest such trajectory
analyses may be useful for predicting and preventing future diseases of
individual patients.
progression from the current patient state. Disease correlations and
temporal disease progression (trajectories) have mainly been analysed
with focus on a small number of diseases or using large-scale approaches
without time consideration, exceeding a few years. So far, no
large-scale studies have focused on defining a comprehensive set of
disease trajectories. Here we present a discovery-driven analysis of
temporal disease progression patterns using data from an electronic
health registry covering the whole population of Denmark. We use the
entire spectrum of diseases and convert 14.9 years of registry data on
6.2 million patients into 1,171 significant trajectories. We group these
into patterns centred on a small number of key diagnoses such as
chronic obstructive pulmonary disease (COPD) and gout, which are central
to disease progression and hence important to diagnose early to
mitigate the risk of adverse outcomes. We suggest such trajectory
analyses may be useful for predicting and preventing future diseases of
individual patients.
