Temporal disease trajectories condensed from population-wide registry data covering 6.2 million patients
By A. B. Jensen, P. Moseley, Tudor I. Oprea, S. G. Ellesøe, Robert Eriksson, H. Schmock, Peter Bjødstrup Jensen, Lars Juhl Jensen, Søren Brunak. 2014
402 citations recorded by the source corpus.
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. Population-wide analyses of healthcare data are challenging and thus often involve only few diseases or comparatively short time scales. Here the authors use healthcare records of all hospital encounters in Denmark over 15 years to describe how disease diagnoses progress over time.
Subfields: Epidemiology, Health, Oncology
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In Epidemiology
Citations in the corpus
- Opportunities and obstacles for deep learning in biology and medicine (2017, background): “Efforts to model sequences of events have shown promise [136] but require exceedingly large patient sizes due to discrete combinatorial bucketing.”
- DeepCare: A Deep Dynamic Memory Model for Predictive Medicine (2016, background): “Capturing disease progression has been of great interest [27, 28], and much effort has been spent on Markov models [7, 29] and dynamic Bayesian networks [30].”
- GRU-ODE-Bayes: Continuous modeling of sporadically-observed time series (2019, background): “Multivariate time series are ubiquitous in various domains of science, such as healthcare (Jensen et al., 2014), astronomy (Scargle, 1982), or climate science (Schneider, 2001).”
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