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’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 re...
| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2022-07-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-022-15764-9 |
| _version_ | 1811218236529704960 |
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| author | 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 |
| author_facet | 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 |
| author_sort | Italo Faria do Valle |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-12T07:07:39Z |
| format | Article |
| id | doaj.art-d1a1df5fcb16439eb4d791e2f8c0f53e |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-12T07:07:39Z |
| publishDate | 2022-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-d1a1df5fcb16439eb4d791e2f8c0f53e2022-12-22T03:42:45ZengNature PortfolioScientific Reports2045-23222022-07-0112111010.1038/s41598-022-15764-9Network-medicine framework for studying disease trajectories in U.S. veteransItalo Faria do Valle0Brian Ferolito1Hanna Gerlovin2Lauren Costa3Serkalem Demissie4Franciel Linares5Jeremy Cohen6David R. Gagnon7J. Michael Gaziano8Edmon Begoli9Kelly Cho10Albert-László Barabási11Center for Complex Network Research, Department of Physics, Northeastern UniversityMassachusetts Veterans Epidemiology and Research Information Center (MAVERIC), VA Boston Healthcare SystemMassachusetts Veterans Epidemiology and Research Information Center (MAVERIC), VA Boston Healthcare SystemMassachusetts Veterans Epidemiology and Research Information Center (MAVERIC), VA Boston Healthcare SystemMassachusetts Veterans Epidemiology and Research Information Center (MAVERIC), VA Boston Healthcare SystemOak Ridge National LaboratoryOak Ridge National LaboratoryMassachusetts Veterans Epidemiology and Research Information Center (MAVERIC), VA Boston Healthcare SystemMassachusetts Veterans Epidemiology and Research Information Center (MAVERIC), VA Boston Healthcare SystemOak Ridge National LaboratoryMassachusetts Veterans Epidemiology and Research Information Center (MAVERIC), VA Boston Healthcare SystemCenter for Complex Network Research, Department of Physics, Northeastern UniversityAbstract 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.https://doi.org/10.1038/s41598-022-15764-9 |
| spellingShingle | 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 Network-medicine framework for studying disease trajectories in U.S. veterans Scientific Reports |
| 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 |
| url | https://doi.org/10.1038/s41598-022-15764-9 |
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