Bridge Centrality: A Network Approach to Understanding Comorbidity presents four network statistics for identifying bridge symptoms in mental disorder networks. The statistics, developed by Payton J. Jones, were tested in simulations achieving an average sensitivity of 92.7% and specificity of 84.9%. The paper applies the algorithms to 18 group-level empirical comorbidity networks from published studies.
Use Cases
- Identifying bridge symptoms between mental disorders based on the described network statistics.
- Simulating the contagion of one mental disorder to another to test intervention strategies.
- Comparing the effectiveness of bridge statistics against traditional centrality measures in preventing comorbidity spread.
- Applying bridge centrality algorithms to group-level empirical comorbidity networks from published literature.
Strengths
- Statistics were validated in simulations with a reported average sensitivity of 92.7%.
- Statistics were validated in simulations with a reported average specificity of 84.9%.
- Algorithms were applied to 18 group-level empirical comorbidity networks from published studies.
Limitations
- Row count and dataset size are unknown, which may limit suitability assessment.
- Column-level documentation is absent; field semantics must be inferred after download.
- Last update date is unknown; freshness unverified.
Provenance
- Source
- Payton J. Jones
- Collection Method
- Methodological development and application to published empirical networks.