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Toward Clinical Digital Twins: A Three-Dimensional Framework for Knowledge Extraction, Pathway Modeling, and

Ankica Babic1,2, William Røise1, Carl Oskar Kraft Sahlgaard1

  • 1Department of Information Science and Media Studies, University of Bergen, Norway.

Studies in Health Technology and Informatics
|May 23, 2026
PubMed
Summary
This summary is machine-generated.

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This study introduces a framework for creating clinical digital twins from real-world data. It combines data analysis and visualization to model patient pathways and support predictive modeling.

Area of Science:

  • Healthcare Informatics
  • Artificial Intelligence in Medicine
  • Data Science

Background:

  • Digital twins offer potential for personalized medicine and improved clinical decision-making.
  • Current challenges include data integration, pathway modeling, and visualization for clinical applications.

Purpose of the Study:

  • To explore the development of clinical digital twins by integrating knowledge extraction, clinical pathway modeling, and visualization.
  • To propose a framework for initializing digital twins from real-world clinical data.

Main Methods:

  • Utilized the Medical Information Mart for Intensive Care IV (MIMIC-IV) database.
  • Employed unsupervised learning, event-log analysis, and visualization design.
  • Developed a framework integrating patient similarity modeling, temporal pathway reconstruction, and user-oriented visualization.
Keywords:
Digital TwinMIMIC-IVarthroplastyclinical pathwaydata miningvisualization

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Main Results:

  • Demonstrated a method for initializing digital twins from clinical data.
  • Illustrated the potential for early predictive modeling using instantiated digital twins.
  • Highlighted the importance of data granularity, temporal structure, and interpretability.

Conclusions:

  • The proposed framework provides a foundation for building clinical digital twins.
  • Digital twins can bridge analytical and clinical perspectives, aiding in healthcare advancements.
  • Iterative refinement and richer data inclusion are key for future development.