Disease trajectory browser for exploring temporal, population-wide disease progression patterns in 7.2 million Danish patients

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

Standard

Disease trajectory browser for exploring temporal, population-wide disease progression patterns in 7.2 million Danish patients. / Siggaard, Troels; Reguant, Roc; Jørgensen, Isabella F.; Haue, Amalie D.; Lademann, Mette; Aguayo-Orozco, Alejandro; Hjaltelin, Jessica X.; Jensen, Anders Boeck; Banasik, Karina; Brunak, Søren.

In: Nature Communications, Vol. 11, No. 1, 4952, 2020.

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

Harvard

Siggaard, T, Reguant, R, Jørgensen, IF, Haue, AD, Lademann, M, Aguayo-Orozco, A, Hjaltelin, JX, Jensen, AB, Banasik, K & Brunak, S 2020, 'Disease trajectory browser for exploring temporal, population-wide disease progression patterns in 7.2 million Danish patients', Nature Communications, vol. 11, no. 1, 4952. https://doi.org/10.1038/s41467-020-18682-4

APA

Siggaard, T., Reguant, R., Jørgensen, I. F., Haue, A. D., Lademann, M., Aguayo-Orozco, A., Hjaltelin, J. X., Jensen, A. B., Banasik, K., & Brunak, S. (2020). Disease trajectory browser for exploring temporal, population-wide disease progression patterns in 7.2 million Danish patients. Nature Communications, 11(1), [4952]. https://doi.org/10.1038/s41467-020-18682-4

Vancouver

Siggaard T, Reguant R, Jørgensen IF, Haue AD, Lademann M, Aguayo-Orozco A et al. Disease trajectory browser for exploring temporal, population-wide disease progression patterns in 7.2 million Danish patients. Nature Communications. 2020;11(1). 4952. https://doi.org/10.1038/s41467-020-18682-4

Author

Siggaard, Troels ; Reguant, Roc ; Jørgensen, Isabella F. ; Haue, Amalie D. ; Lademann, Mette ; Aguayo-Orozco, Alejandro ; Hjaltelin, Jessica X. ; Jensen, Anders Boeck ; Banasik, Karina ; Brunak, Søren. / Disease trajectory browser for exploring temporal, population-wide disease progression patterns in 7.2 million Danish patients. In: Nature Communications. 2020 ; Vol. 11, No. 1.

Bibtex

@article{d6b11b9c4fab455cbcf1f6afab6f1ccc,
title = "Disease trajectory browser for exploring temporal, population-wide disease progression patterns in 7.2 million Danish patients",
abstract = "We present the Danish Disease Trajectory Browser (DTB), a tool for exploring almost 25 years of data from the Danish National Patient Register. In the dataset comprising 7.2 million patients and 122 million admissions, users can identify diagnosis pairs with statistically significant directionality and combine them to linear disease trajectories. Users can search for one or more disease codes (ICD-10 classification) and explore disease progression patterns via an array of functionalities. For example, a set of linear trajectories can be merged into a disease trajectory network displaying the entire multimorbidity spectrum of a disease in a single connected graph. Using data from the Danish Register for Causes of Death mortality is also included. The tool is disease-agnostic across both rare and common diseases and is showcased by exploring multimorbidity in Down syndrome (ICD-10 code Q90) and hypertension (ICD-10 code I10). Finally, we show how search results can be customized and exported from the browser in a format of choice (i.e. JSON, PNG, JPEG and CSV). The Danish health system has been collecting health-related data on the entire Danish population for years. Here the authors present the Danish Disease Trajectory Browser (DTB), which allows users to explore population-wide disease progression patterns from data collected between 1994 and 2018.",
keywords = "REGISTRY, EPIDEMIOLOGY, COMORBIDITY, ONTOLOGY, CARE",
author = "Troels Siggaard and Roc Reguant and J{\o}rgensen, {Isabella F.} and Haue, {Amalie D.} and Mette Lademann and Alejandro Aguayo-Orozco and Hjaltelin, {Jessica X.} and Jensen, {Anders Boeck} and Karina Banasik and S{\o}ren Brunak",
year = "2020",
doi = "10.1038/s41467-020-18682-4",
language = "English",
volume = "11",
journal = "Nature Communications",
issn = "2041-1723",
publisher = "nature publishing group",
number = "1",

}

RIS

TY - JOUR

T1 - Disease trajectory browser for exploring temporal, population-wide disease progression patterns in 7.2 million Danish patients

AU - Siggaard, Troels

AU - Reguant, Roc

AU - Jørgensen, Isabella F.

AU - Haue, Amalie D.

AU - Lademann, Mette

AU - Aguayo-Orozco, Alejandro

AU - Hjaltelin, Jessica X.

AU - Jensen, Anders Boeck

AU - Banasik, Karina

AU - Brunak, Søren

PY - 2020

Y1 - 2020

N2 - We present the Danish Disease Trajectory Browser (DTB), a tool for exploring almost 25 years of data from the Danish National Patient Register. In the dataset comprising 7.2 million patients and 122 million admissions, users can identify diagnosis pairs with statistically significant directionality and combine them to linear disease trajectories. Users can search for one or more disease codes (ICD-10 classification) and explore disease progression patterns via an array of functionalities. For example, a set of linear trajectories can be merged into a disease trajectory network displaying the entire multimorbidity spectrum of a disease in a single connected graph. Using data from the Danish Register for Causes of Death mortality is also included. The tool is disease-agnostic across both rare and common diseases and is showcased by exploring multimorbidity in Down syndrome (ICD-10 code Q90) and hypertension (ICD-10 code I10). Finally, we show how search results can be customized and exported from the browser in a format of choice (i.e. JSON, PNG, JPEG and CSV). The Danish health system has been collecting health-related data on the entire Danish population for years. Here the authors present the Danish Disease Trajectory Browser (DTB), which allows users to explore population-wide disease progression patterns from data collected between 1994 and 2018.

AB - We present the Danish Disease Trajectory Browser (DTB), a tool for exploring almost 25 years of data from the Danish National Patient Register. In the dataset comprising 7.2 million patients and 122 million admissions, users can identify diagnosis pairs with statistically significant directionality and combine them to linear disease trajectories. Users can search for one or more disease codes (ICD-10 classification) and explore disease progression patterns via an array of functionalities. For example, a set of linear trajectories can be merged into a disease trajectory network displaying the entire multimorbidity spectrum of a disease in a single connected graph. Using data from the Danish Register for Causes of Death mortality is also included. The tool is disease-agnostic across both rare and common diseases and is showcased by exploring multimorbidity in Down syndrome (ICD-10 code Q90) and hypertension (ICD-10 code I10). Finally, we show how search results can be customized and exported from the browser in a format of choice (i.e. JSON, PNG, JPEG and CSV). The Danish health system has been collecting health-related data on the entire Danish population for years. Here the authors present the Danish Disease Trajectory Browser (DTB), which allows users to explore population-wide disease progression patterns from data collected between 1994 and 2018.

KW - REGISTRY

KW - EPIDEMIOLOGY

KW - COMORBIDITY

KW - ONTOLOGY

KW - CARE

U2 - 10.1038/s41467-020-18682-4

DO - 10.1038/s41467-020-18682-4

M3 - Journal article

C2 - 33009368

VL - 11

JO - Nature Communications

JF - Nature Communications

SN - 2041-1723

IS - 1

M1 - 4952

ER -

ID: 250484923