ArticleScientific reports2022
Network-medicine framework for studying disease trajectories in U.S. veterans.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Temporal multimorbidity patterns and cluster identification: a longitudinal analysis of administrative data.BMC medicine · 2025Article
- Disease Network-Based Approaches to Study Comorbidity in Heart Failure: Current State and Future Perspectives.Current heart failure reports · 2024Review
- Systematic review and meta-analysis of disease clustering in multimorbidity: a study protocol.BMJ open · 2023Article
- Noncoding RNAs improve the predictive power of network medicine.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Opportunities and challenges for biomarker discovery using electronic health record data.Trends in molecular medicine · 2023Review
- Analysis of age-dependent gene-expression in human tissues for studying diabetes comorbidities.Scientific reports · 2023Article
- Article
- DETECT: Feature extraction method for disease trajectory modeling in electronic health records.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2023Article
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Authors and funding
12 authors.
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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.
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Registered trials
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