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Computational Modeling Expert Mobilizes National Committee to Create Best Practices

Dr. Erdemir played a key role in developing 10 rules to ensure the credibility of healthcare-related computational simulation and models.

12/03/2020

Computational modeling and simulation have long been used in research and engineering, and are increasingly employed to predict the origin and spread of diseases.

With the growing popularity of computational modeling and its role in shaping treatment and policy decisions, standardization protocols are needed to deal with the complexities of modern biomedicine. As such, the national, interdisciplinary Committee on Credible Practice of Modeling & Simulation in Healthcare has created a list of 10 rules to establish modeling credibility, encompassing reproducibility, validity, accountability and outreach. The list was published in the Journal of Translational Medicine.

Ahmet Erdemir, PhD, associate staff in the Department of Biomedical Engineering and founding co-chair and active member of the committee, is lead author of the article. “Computational modeling and simulation are driving healthcare decisions more than ever before,” Dr. Erdemir noted. “The need for credible and reliable execution and interpretation has taken on a new urgency.”

The list of rules from the committee and their descriptions are as follows:

  1. Define context clearly. The authors point out that this first rule affects the implementation of several other rules, including the use of appropriate data and evaluating results within the scope of the model’s intended use.
  2. Use contextually appropriate data. This refers to the ability to understand and trace all of the data back to its original source, and appropriately leverage it for modeling and simulation.
  3. Evaluate within context. Includes verifying and validating all of the data and modeling activities, and applying them to evaluation of simulation outcomes.
  4. List limitations explicitly. Specifies which of the model’s conditions or qualifications cannot be relied on, and why.
  5. Use version control. Ensures that others can trace the modeling activities and identify the contributions of all participants.
  6. Document appropriately. Provides future users with all of the information used in the modeling and simulation to assess and verify its credibility and to reuse.
  7. Disseminate broadly. This involves the sharing of all pertinent activities and components of the model, including software, scenarios and results, to promote transparency and to facilitate reuse.
  8. Get independent reviews. This builds trust and credibility by allowing non-partisan third-party reviewers to evaluate the methods of modeling and/or simulation.
  9. Test competing implementations. Comparing the current model and/or simulation with an earlier model to allow the practitioners to assess its evolution and likelihood of effectiveness.
  10. Conform to standards. Ensures that the model or simulation adheres to appropriate guidelines and procedures, especially those relating to that particular discipline.

“Computational modeling is an indispensable strategy for biomedical research, individualized clinical decision-making and medical training,” said Dr. Erdemir. “The power of simulations cannot be realized without establishing their dependability and reliability. This study establishes broadly applicable, discipline-agnostic and usable guidance for building, utilizing and interpreting healthcare-related computational models.”

This project was supported by the National Institutes of Health.

Image: In a synergistic project funded by the National Institutes of Health, Dr. Erdemir and collaborators have been investigating the "art" of modeling and simulation. Using the same data and targeting the same intention for simulations, five teams have been building knee joint models. The activities demonstrate the relevance of the Ten Rules of credible practice; in this case, testing competing implementations in knee joint modeling. (Image adapted from Erdemir et al., J Biomech Eng, 2019, 141, 0710021-07100210; https://doi.org/10.1115/1.4043346.)

 



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