Addressing Clinical Problems with Graph Representations of Clinical Data

Healthcare data are inherently relational, hierarchical, and dynamic. Traditional tabular representations often fail to capture interactions across biological, clinical, and system levels. Graph-based models, and in general network-inspired ones, offer a natural way to represent such complexity by encoding entities and their relationships in a flexible and expressive form. In recent years, network science, network medicine, connectomics, and graph-based machine learning have demonstrated the value of this perspective across diverse healthcare applications. At the same time, emerging data sources, such as longitudinal electronic health records, multimodal biomedical data, and complex care pathways, further motivate graph-based abstractions. Despite this progress, the design, interpretation, and validation of healthcare graphs remain open challenges. Therefore, there is a need for a dedicated forum that highlights graph representations themselves as central scientific objects in healthcare research.



Networks, from graphs to higher-order models, have become a powerful and unifying representation for studying health and disease. Across biomedical research, clinical practice, and healthcare systems, complex entities and interactions, such as genes, brain regions, patients, care events, and clinical teams, are increasingly modeled as nodes connected by meaningful relationships. However, graph-based approaches in healthcare are often developed in isolation within specific domains, with limited cross-fertilization of ideas, representations, and methodological insights. This Research Topic aims to bring together researchers who explicitly use graph representations as a primary modeling framework to study healthcare-related problems. By focusing on how health phenomena can be encoded, analyzed, and interpreted through graphs, this collection seeks to advance conceptual clarity, methodological rigor, and translational impact. Contributions are encouraged that move beyond predictive performance to examine what graph structure, topology, and dynamics reveal about health, disease, and care delivery.


This Research Topic welcomes contributions that use network representations to study healthcare, medicine, or biomedical phenomena at any scale.

Topics of interest include (but are not limited to):
• Patient similarity and cohort graphs
• Disease progression and care pathway graphs
• Molecular, cellular, and omics networks
• Brain and neural connectivity networks
• Healthcare delivery, workflow, and referral networks
• Multilayer, temporal, or heterogeneous graphs
• Graph topology and structural biomarkers
• Interpretable or theory-informed graph learning methods

We particularly encourage submissions that emphasize representation choices, structural insights, and clinical/biological interpretation (beyond predictive performance). Manuscript types may include original research, methodological/technical papers, data/resource articles, reviews, and perspectives, with interdisciplinary work bridging clinical practice, biomedical science, data science, and network theory especially encouraged.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Data Report
• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Data Report
• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Review
• Systematic Review
• Technology and Code

Keywords: Graph representation, Networks, Healthcare networks, Patient similarity graphs, Clinical pathways, Graph topology, Multilayer networks, Temporal networks, Graph neural networks, Explainable AI in healthcare

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.