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Disease trajectory browser for exploring temporal, population-wide disease progression patterns in 7.2 million Danish patients
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...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7532164/ https://www.ncbi.nlm.nih.gov/pubmed/33009368 http://dx.doi.org/10.1038/s41467-020-18682-4 |
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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 |
author_facet | 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 |
author_sort | Siggaard, Troels |
collection | PubMed |
description | 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). |
format | Online Article Text |
id | pubmed-7532164 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-75321642020-10-19 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 Nat Commun Article 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). Nature Publishing Group UK 2020-10-02 /pmc/articles/PMC7532164/ /pubmed/33009368 http://dx.doi.org/10.1038/s41467-020-18682-4 Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article 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 |
title | Disease trajectory browser for exploring temporal, population-wide disease progression patterns in 7.2 million Danish patients |
title_full | Disease trajectory browser for exploring temporal, population-wide disease progression patterns in 7.2 million Danish patients |
title_fullStr | Disease trajectory browser for exploring temporal, population-wide disease progression patterns in 7.2 million Danish patients |
title_full_unstemmed | Disease trajectory browser for exploring temporal, population-wide disease progression patterns in 7.2 million Danish patients |
title_short | Disease trajectory browser for exploring temporal, population-wide disease progression patterns in 7.2 million Danish patients |
title_sort | disease trajectory browser for exploring temporal, population-wide disease progression patterns in 7.2 million danish patients |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7532164/ https://www.ncbi.nlm.nih.gov/pubmed/33009368 http://dx.doi.org/10.1038/s41467-020-18682-4 |
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