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Network-medicine framework for studying disease trajectories in U.S. veterans
A better understanding of the sequential and temporal aspects in which diseases occur in patient’s lives is essential for developing improved intervention strategies that reduce burden and increase the quality of health services. Here we present a network-based framework to study disease relationshi...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9283486/ https://www.ncbi.nlm.nih.gov/pubmed/35835798 http://dx.doi.org/10.1038/s41598-022-15764-9 |
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author | do Valle, Italo Faria Ferolito, Brian Gerlovin, Hanna Costa, Lauren Demissie, Serkalem Linares, Franciel Cohen, Jeremy Gagnon, David R. Gaziano, J. Michael Begoli, Edmon Cho, Kelly Barabási, Albert-László |
author_facet | do Valle, Italo Faria Ferolito, Brian Gerlovin, Hanna Costa, Lauren Demissie, Serkalem Linares, Franciel Cohen, Jeremy Gagnon, David R. Gaziano, J. Michael Begoli, Edmon Cho, Kelly Barabási, Albert-László |
author_sort | do Valle, Italo Faria |
collection | PubMed |
description | A better understanding of the sequential and temporal aspects in which diseases occur in patient’s lives is essential for developing improved intervention strategies that reduce burden and increase the quality of health services. Here we present a network-based framework to study disease relationships using Electronic Health Records from > 9 million patients in the United States Veterans Health Administration (VHA) system. We create the Temporal Disease Network, which maps the sequential aspects of disease co-occurrence among patients and demonstrate that network properties reflect clinical aspects of the respective diseases. We use the Temporal Disease Network to identify disease groups that reflect patterns of disease co-occurrence and the flow of patients among diagnoses. Finally, we define a strategy for the identification of trajectories that lead from one disease to another. The framework presented here has the potential to offer new insights for disease treatment and prevention in large health care systems. |
format | Online Article Text |
id | pubmed-9283486 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-92834862022-07-16 Network-medicine framework for studying disease trajectories in U.S. veterans do Valle, Italo Faria Ferolito, Brian Gerlovin, Hanna Costa, Lauren Demissie, Serkalem Linares, Franciel Cohen, Jeremy Gagnon, David R. Gaziano, J. Michael Begoli, Edmon Cho, Kelly Barabási, Albert-László Sci Rep Article A better understanding of the sequential and temporal aspects in which diseases occur in patient’s lives is essential for developing improved intervention strategies that reduce burden and increase the quality of health services. Here we present a network-based framework to study disease relationships using Electronic Health Records from > 9 million patients in the United States Veterans Health Administration (VHA) system. We create the Temporal Disease Network, which maps the sequential aspects of disease co-occurrence among patients and demonstrate that network properties reflect clinical aspects of the respective diseases. We use the Temporal Disease Network to identify disease groups that reflect patterns of disease co-occurrence and the flow of patients among diagnoses. Finally, we define a strategy for the identification of trajectories that lead from one disease to another. The framework presented here has the potential to offer new insights for disease treatment and prevention in large health care systems. Nature Publishing Group UK 2022-07-14 /pmc/articles/PMC9283486/ /pubmed/35835798 http://dx.doi.org/10.1038/s41598-022-15764-9 Text en © This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply 2022 https://creativecommons.org/licenses/by/4.0/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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article do Valle, Italo Faria Ferolito, Brian Gerlovin, Hanna Costa, Lauren Demissie, Serkalem Linares, Franciel Cohen, Jeremy Gagnon, David R. Gaziano, J. Michael Begoli, Edmon Cho, Kelly Barabási, Albert-László Network-medicine framework for studying disease trajectories in U.S. veterans |
title | Network-medicine framework for studying disease trajectories in U.S. veterans |
title_full | Network-medicine framework for studying disease trajectories in U.S. veterans |
title_fullStr | Network-medicine framework for studying disease trajectories in U.S. veterans |
title_full_unstemmed | Network-medicine framework for studying disease trajectories in U.S. veterans |
title_short | Network-medicine framework for studying disease trajectories in U.S. veterans |
title_sort | network-medicine framework for studying disease trajectories in u.s. veterans |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9283486/ https://www.ncbi.nlm.nih.gov/pubmed/35835798 http://dx.doi.org/10.1038/s41598-022-15764-9 |
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