ArticleApplied network science2018
Statistical methods for constructing disease comorbidity networks from longitudinal inpatient data.
Article in Applied network science, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 1 of them a synthesis that pooled it.
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Who cites it
30 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic review and meta-analysis of disease clusters in multimorbidity.Nature communications · 2025Pooled it
- A Network-Medicine Framework for Intra-Oral Comorbidity: Age-Stratified Clustering and Quasi-Causal Progression Modeling from Outpatient Electronic Health Records.Bioengineering (Basel, Switzerland) · 2026Article
- Detecting comorbidity patterns in rare disease patients with machine learning.Frontiers in epidemiology · 2026Article
- Constructing the first comorbidity networks in companion dogs in the Dog Aging Project.PLoS computational biology · 2025Article
- Constructing the first comorbidity networks in companion dogs in the Dog Aging Project.bioRxiv : the preprint server for biology · 2025Article
- Temporal multimorbidity patterns and cluster identification: a longitudinal analysis of administrative data.BMC medicine · 2025Article
- Large-scale evidence of a general disease ('Psychological medicine · 2025Article
- Exploring comorbidity networks in mild traumatic brain injury subjects through graph theory: a traumatic brain injury model systems study.BMC neurology · 2025Article
- Comorbidity Networks From Population-Wide Health Data: Aggregated Data of 8.9M Hospital Patients (1997-2014).Scientific data · 2025Article
- Evaluating the kidney disease progression using a comprehensive patient profiling algorithm: A hybrid clustering approach.PloS one · 2025Article
- Disease Network-Based Approaches to Study Comorbidity in Heart Failure: Current State and Future Perspectives.Current heart failure reports · 2024Review
- PheMIME: an interactive web app and knowledge base for phenome-wide, multi-institutional multimorbidity analysis.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Choices of measures of association affect the visualisation and composition of the multimorbidity networks.BMC medical research methodology · 2024Article
- A network-based approach to explore comorbidity patterns among community-dwelling older adults living alone.GeroScience · 2024Article
- Unraveling cradle-to-grave disease trajectories from multilayer comorbidity networks.NPJ digital medicine · 2024Article
- Acute kidney injury comorbidity analysis based on international classification of diseases-10 codes.BMC medical informatics and decision making · 2024Article
- PheMIME: An Interactive Web App and Knowledge Base for Phenome-Wide, Multi-Institutional Multimorbidity Analysis.medRxiv : the preprint server for health sciences · 2023Article
- Comorbidity network analysis using graphical models for electronic health records.Frontiers in big data · 2023Article
- Prevalence of comorbidities post mild traumatic brain injuries: a traumatic brain injury model systems study.Frontiers in human neuroscience · 2023Article
- The Use of a Network Analysis to Identify Associations and Temporal Patterns Among Non-communicable Diseases in Japan Based on a Large Medical Claims Database.Drugs - real world outcomes · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Tools from network science can be utilized to study relations between diseases. Different studies focus on different types of inter-disease linkages. One of them is the comorbidity patterns derived from large-scale longitudinal data of hospital discharge records. Researchers seek to describe comorbidity relations as a network to characterize pathways of disease progressions and to predict future risks. The first step in such studies is the construction of the network itself, which subsequent analyses rest upon. There are different ways to build such a network. In this paper, we provide an overview of several existing statistical approaches in network science applicable to weighted directed networks. We discuss the differences between the null models that these models assume and their applications. We apply these methods to the inpatient data of approximately one million people, spanning approximately 17 years, pertaining to the Montreal Census Metropolitan Area. We discuss the differences in the structure of the networks built by different methods, and different features of the comorbidity relations that they extract. We also present several example applications of these methods.
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