A modular computational framework for medical digital twins
Open accessAbstract
Significance: The digital twin paradigm holds great promise for medicine, even though many technical and scientific challenges remain to be overcome, most importantly the efficient integration of many heterogeneous component models. This is an unsolved problem even in industry. It has long been understood that such models need to be built in a modular fashion, connecting together component models of individual biological processes. In conventional implementations, however, the dependency structure of the modules reflects the dependencies among these processes, making it all but impossible to modify or expand the digital twin without breaking it. This paper presents a fundamental reorganization of the internal digital twin architecture into a hub-and-spokes design that completely circumvents this dependency problem.
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An open-source modular framework has also been proposed for the development of medical digital twin models.
“An open-source modular framework has also been proposed for the development of medical digital twin models 69 .”
From Multimodal biomedical AI (2022) · cites this paper as [69]
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Qu, G., Ribeiro, H. A., Solomon, A. L., Vieira, L. S., Goddard, Y., Diodati, N. G., Lazarte, A. V., Wheeler, M., Laubenbacher, R., & Mehrad, B. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.08.06.606866
Self-citationExploration on Security Monitoring Methods for Power Mobile Terminals Under Modular Architecture
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Foundations and applications of computational genomics
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The Role of Metaverse Platforms and Digital Twins
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Host Immune Defense Regulation Against Respiratory Infections
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Chen, R., Bartelo, N., Arikatla, M. R., Mason, C. E., & Elemento, O. (2025) · medRxiv · doi:10.1101/2025.11.27.25341167
- Authors
- Masison, J., Beezley, J., Mei, Y., Ribeiro, H., Knapp, A. C., Sordo Vieira, L., Adhikari, B., Scindia, Y., Grauer, M., Helba, B., Schroeder, W., Mehrad, B., & Laubenbacher, R.
- Affiliations
- UConn Health (US)
- Kitware (United States) (US)
- University of Michigan (US)
- Department of Medicine, University of Florida, Gainesville, FL 32611
- University of Florida (US)
- Corresponding author
- Laubenbacher, R.
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 2021-05-10
- Volume
- 118
- Issue
- 20
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- PMC
- PMC8157963
- OpenAlex
- W3163235749
- Cited by
- 98
- References
- 18
- Citations by year
- 2026: 14 · 2025: 25 · 2024: 18 · 2023: 19 · 2022: 19 · 2021: 3
- FWCI
- 16.25
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Proceedings of the National Academy of Sciences (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- HHS | NIH | National Institute of Biomedical Imaging and Bioengineering (1U01EB024501-01)
- HHS | NIH | National Institute of Allergy and Infectious Diseases (1 R01AI135128-01)
- HHS | NIH | National Institute of General Medical Sciences (1R01GM127909-01)
- HHS | NIH | National Institute of Dental and Craniofacial Research (T-90DE021989)
- American Heart Association (18TPA34170486)
- Topics
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Technology and Human Factors in Education and Health
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Modeling and Simulation Systems
- Social Sciences › Decision Sciences › Information Systems and Management › Scientific Computing and Data Management
- Keywords
- medical digital twins, modular software platform, precision medicine, patient-specific disease modeling, model integration, data-driven modeling, mechanistic modeling, open source software, respiratory fungal infection, personalized treatment
- MeSH terms
- Algorithms, Animals, Antifungal Agents, Aspergillosis, Aspergillus fumigatus, Humans, Software, Spores, Fungal, Computational Biology, Precision Medicine, Patient-Specific Modeling
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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