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

Anders Boeck Jensen, Pope L. Moseley, Tudor I. Oprea, Sabrina Gade Ellesøe, Robert Eriksson, Henriette Schmock, Peter Bjødstrup Jensen, Lars Juhl Jensen () and Søren Brunak ()
Additional contact information
Anders Boeck Jensen: Center for Biological Sequence Analysis, Technical University of Denmark
Pope L. Moseley: NNF Center for Protein Research, University of Copenhagen
Tudor I. Oprea: Center for Biological Sequence Analysis, Technical University of Denmark
Sabrina Gade Ellesøe: NNF Center for Protein Research, University of Copenhagen
Robert Eriksson: Center for Biological Sequence Analysis, Technical University of Denmark
Henriette Schmock: Institute of Biological Psychiatry, Mental Health Center Sct. Hans, Copenhagen University Hospital
Peter Bjødstrup Jensen: NNF Center for Protein Research, University of Copenhagen
Lars Juhl Jensen: NNF Center for Protein Research, University of Copenhagen
Søren Brunak: Center for Biological Sequence Analysis, Technical University of Denmark

Nature Communications, 2014, vol. 5, issue 1, 1-10

Abstract: 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.

Date: 2014
References: Add references at CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
https://www.nature.com/articles/ncomms5022 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:5:y:2014:i:1:d:10.1038_ncomms5022

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/ncomms5022

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().