Network-medicine framework for studying disease trajectories in U.S. veterans

Italo Faria do Valle; Brian Ferolito; Hanna Gerlovin; Lauren Costa; Serkalem Demissie; Franciel Linares; Jeremy Cohen; David R. Gagnon; J. Michael Gaziano; Edmon Begoli; Kelly Cho; Albert László Barabási

doi:10.1038/s41598-022-15764-9

Network-medicine framework for studying disease trajectories in U.S. veterans

Italo Faria do Valle, Brian Ferolito, Hanna Gerlovin, Lauren Costa, Serkalem Demissie, Franciel Linares, Jeremy Cohen, David R. Gagnon, J. Michael Gaziano, Edmon Begoli, Kelly Cho, Albert László Barabási

Cyber Resilience and Intelligence Divisi

Research output: Contribution to journal › Article › peer-review

6 Scopus citations

Abstract

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.

Original language	English
Article number	12018
Journal	Scientific Reports
Volume	12
Issue number	1
DOIs	https://doi.org/10.1038/s41598-022-15764-9
State	Published - Dec 2022

Funding

This research was supported by award #MVP000 from the VA Million Veteran Program, Office of Research and Development, Veterans Health Administration VA Central Institutional Review Board (IRB). This manuscript has been in part co-authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725, and under a joint program with the Department of Veterans Affairs under the Million Veteran Program Computational Health Analytics for Medical Precision to Improve Outcomes Now. This research used resources of the Knowledge Discovery Infrastructure at the Oak Ridge National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725. This publication does not represent the views of the Department of Veterans Affairs, the Department of Energy or the U.S. government. This research was supported by award #MVP000 from the VA Million Veteran Program, Office of Research and Development, Veterans Health Administration VA Central Institutional Review Board (IRB). This manuscript has been in part co-authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725, and under a joint program with the Department of Veterans Affairs under the Million Veteran Program Computational Health Analytics for Medical Precision to Improve Outcomes Now. This research used resources of the Knowledge Discovery Infrastructure at the Oak Ridge National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725. This publication does not represent the views of the Department of Veterans Affairs, the Department of Energy or the U.S. government.

Funders	Funder number
U.S. Government
VA Million Veteran Program
Veterans Health Administration VA
U.S. Department of Energy
U.S. Department of Veterans Affairs
Office of Science
Office of Research and Development
UT-Battelle	DE-AC05-00OR22725

Access to Document

10.1038/s41598-022-15764-9

Cite this

@article{abf41527ada944498822c5c972438da9,

title = "Network-medicine framework for studying disease trajectories in U.S. veterans",

abstract = "A better understanding of the sequential and temporal aspects in which diseases occur in patient{\textquoteright}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.",

author = "{do Valle}, {Italo Faria} and Brian Ferolito and Hanna Gerlovin and Lauren Costa and Serkalem Demissie and Franciel Linares and Jeremy Cohen and Gagnon, {David R.} and Gaziano, {J. Michael} and Edmon Begoli and Kelly Cho and Barab{\'a}si, {Albert L{\'a}szl{\'o}}",

note = "Publisher Copyright: {\textcopyright} 2022, This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.",

year = "2022",

month = dec,

doi = "10.1038/s41598-022-15764-9",

language = "English",

volume = "12",

journal = "Scientific Reports",

issn = "2045-2322",

publisher = "Nature Research",

number = "1",

}

TY - JOUR

T1 - Network-medicine framework for studying disease trajectories in U.S. veterans

AU - do Valle, Italo Faria

AU - Ferolito, Brian

AU - Gerlovin, Hanna

AU - Costa, Lauren

AU - Demissie, Serkalem

AU - Linares, Franciel

AU - Cohen, Jeremy

AU - Gagnon, David R.

AU - Gaziano, J. Michael

AU - Begoli, Edmon

AU - Cho, Kelly

AU - Barabási, Albert László

PY - 2022/12

Y1 - 2022/12

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85134086132&partnerID=8YFLogxK

U2 - 10.1038/s41598-022-15764-9

DO - 10.1038/s41598-022-15764-9

M3 - Article

C2 - 35835798

AN - SCOPUS:85134086132

SN - 2045-2322

VL - 12

JO - Scientific Reports

JF - Scientific Reports

IS - 1

M1 - 12018

ER -

Network-medicine framework for studying disease trajectories in U.S. veterans

Abstract

Funding

Access to Document

Other files and links

Fingerprint

Cite this