Temporal disease trajectories condensed from population-wide registry data covering 6.2 million patients

Research output: Contribution to journal › Journal article › Research › peer-review

Standard

Temporal disease trajectories condensed from population-wide registry data covering 6.2 million patients. / Jensen, Anders Boeck; Moseley, Pope L; Oprea, Tudor I; Ellesøe, Sabrina Gade; Eriksson, Robert; Schmock, Henriette; Jensen, Peter Bjødstrup; Jensen, Lars Juhl; Brunak, Søren.

In: Nature Communications, Vol. 5, 4022, 24.06.2014.

Research output: Contribution to journal › Journal article › Research › peer-review

Harvard

Jensen, AB, Moseley, PL, Oprea, TI, Ellesøe, SG, Eriksson, R, Schmock, H, Jensen, PB, Jensen, LJ & Brunak, S 2014, 'Temporal disease trajectories condensed from population-wide registry data covering 6.2 million patients', Nature Communications, vol. 5, 4022. https://doi.org/10.1038/ncomms5022

APA

Jensen, A. B., Moseley, P. L., Oprea, T. I., Ellesøe, S. G., Eriksson, R., Schmock, H., Jensen, P. B., Jensen, L. J., & Brunak, S. (2014). Temporal disease trajectories condensed from population-wide registry data covering 6.2 million patients. Nature Communications, 5, [4022]. https://doi.org/10.1038/ncomms5022

Vancouver

Jensen AB, Moseley PL, Oprea TI, Ellesøe SG, Eriksson R, Schmock H et al. Temporal disease trajectories condensed from population-wide registry data covering 6.2 million patients. Nature Communications. 2014 Jun 24;5. 4022. https://doi.org/10.1038/ncomms5022

Author

Jensen, Anders Boeck ; Moseley, Pope L ; Oprea, Tudor I ; Ellesøe, Sabrina Gade ; Eriksson, Robert ; Schmock, Henriette ; Jensen, Peter Bjødstrup ; Jensen, Lars Juhl ; Brunak, Søren. / Temporal disease trajectories condensed from population-wide registry data covering 6.2 million patients. In: Nature Communications. 2014 ; Vol. 5.

Bibtex

@article{8b97a9bf6f244632850d0720ed8e6b72,
title = "Temporal disease trajectories condensed from population-wide registry data covering 6.2 million patients",
abstract = "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.",
author = "Jensen, {Anders Boeck} and Moseley, {Pope L} and Oprea, {Tudor I} and Elles{\o}e, {Sabrina Gade} and Robert Eriksson and Henriette Schmock and Jensen, {Peter Bj{\o}dstrup} and Jensen, {Lars Juhl} and S{\o}ren Brunak",
year = "2014",
month = jun,
day = "24",
doi = "10.1038/ncomms5022",
language = "English",
volume = "5",
journal = "Nature Communications",
issn = "2041-1723",
publisher = "nature publishing group",

}

RIS

TY - JOUR

T1 - Temporal disease trajectories condensed from population-wide registry data covering 6.2 million patients

AU - Jensen, Anders Boeck

AU - Moseley, Pope L

AU - Oprea, Tudor I

AU - Ellesøe, Sabrina Gade

AU - Eriksson, Robert

AU - Schmock, Henriette

AU - Jensen, Peter Bjødstrup

AU - Jensen, Lars Juhl

AU - Brunak, Søren

PY - 2014/6/24

Y1 - 2014/6/24

N2 - 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.

AB - 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.

U2 - 10.1038/ncomms5022

DO - 10.1038/ncomms5022

M3 - Journal article

C2 - 24959948

VL - 5

JO - Nature Communications

JF - Nature Communications

SN - 2041-1723

M1 - 4022

ER -

ID: 117864412