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A modular computational framework for medical digital twins.

J Masison1, J Beezley2, Y Mei3

  • 1Center for Quantitative Medicine, University of Connecticut Health Center, Farmington, CT 06032.

Proceedings of the National Academy of Sciences of the United States of America
|May 11, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a modular software platform for building computational models, or digital twins, to advance precision medicine. This open-source technology enables collaborative development and integration of patient-specific disease models for improved healthcare.

Keywords:
medical digital twinmodular designmultiscale computational model

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Software Engineering

Background:

  • Precision medicine aims to tailor treatments to individual patients.
  • Medical digital twins are computational models of disease processes calibrated to individual patients.
  • Developing these models requires integrating diverse data and component models from multiple sources.

Purpose of the Study:

  • To present a modular, open-source software platform for constructing and integrating computational models for precision medicine.
  • To facilitate a decentralized, community-based approach to developing and updating medical digital twins.
  • To demonstrate the platform's utility through a case study.

Main Methods:

  • Development of a modular software architecture for computational modeling.
  • Integration of mechanistic and data-driven modeling techniques.
  • Implementation of a case study involving an animal model of respiratory fungal infection.

Main Results:

  • A scalable, open-source platform for the integration and simulation of diverse computational models was developed.
  • The platform supports a decentralized, community-driven model-building process.
  • Successful application of the platform in a case study demonstrated its feasibility.

Conclusions:

  • The presented software platform supports the large-scale development of medical digital twins for precision medicine.
  • The modular and open-source nature fosters collaboration and accelerates the implementation of personalized healthcare solutions.
  • This approach has the potential to significantly improve diagnosis, prognosis, and treatment across various medical conditions.