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Personalization of models with many model parameters: an efficient sensitivity analysis approach.

W P Donders1, W Huberts2,3, F N van de Vosse2,3

  • 1Department of Biomedical Engineering, School for Mental Health and Neuroscience (MHENS), Maastricht University, Maastricht, The Netherlands.

International Journal for Numerical Methods in Biomedical Engineering
|May 29, 2015
PubMed
Summary

This study introduces a novel two-step method for efficient uncertainty quantification and global sensitivity analysis in complex models. The approach significantly reduces computational cost while maintaining accurate estimations of Sobol sensitivity indices.

Keywords:
model personalizationpolynomial chaos expansionsensitivity analysisuncertainty quantification

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Area of Science:

  • Computational modeling
  • Biomedical engineering
  • Numerical analysis

Background:

  • Uncertainty quantification and global sensitivity analysis are crucial for patient-specific models in diagnosis and decision-making.
  • Variance-based methods like Sobol indices are gold standards but computationally expensive, requiring numerous model evaluations.
  • Existing methods, including Monte Carlo and generalized polynomial chaos expansion (gPCE), face challenges with models having many parameters.

Purpose of the Study:

  • To develop a computationally efficient two-step approach for uncertainty quantification and global sensitivity analysis.
  • To reduce the number of model evaluations required for estimating Sobol sensitivity indices, especially for models with multiple outputs.
  • To validate the proposed method using a model for predicting post-operative flows in renal failure patients.

Main Methods:

  • A two-step approach combining Morris screening for parameter subset identification and gPCE for quantitative sensitivity analysis.
  • Introduction of efficient sampling strategies to minimize model runs for multi-output sensitivity analysis.
  • Validation against Saltelli's Monte Carlo method using a clinical model for vascular access in renal failure patients.

Main Results:

  • The proposed two-step approach accurately estimates Sobol sensitivity indices.
  • The method achieves computational cost reduction of two orders of magnitude compared to traditional methods.
  • Efficient sampling strategies were successfully implemented for multi-output analysis.

Conclusions:

  • The novel two-step approach offers a computationally efficient and accurate alternative for uncertainty quantification and global sensitivity analysis.
  • This method is particularly beneficial for patient-specific modeling applications requiring high computational performance.
  • The validated approach provides reliable sensitivity indices for complex biomedical models, aiding in diagnosis and decision-making.