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Optimization framework for patient-specific modeling under uncertainty. | LitMetric

AI Article Synopsis

  • Estimating parameters for patient-specific models is challenging due to unreliable data; this study introduces a probabilistic framework to optimize model inputs based on target output distributions.
  • A surrogate model is utilized for efficient probabilistic sampling, allowing for a general representation of each input's uncertainty.
  • The approach was successfully tested on seven modeling examples, accurately replicating output distributions and input parameters, demonstrating its effectiveness in handling uncertainties in patient-specific computational models.

Article Abstract

Estimating a patient-specific computational model's parameters relies on data that is often unreliable and ill-suited for a deterministic approach. We develop an optimization-based uncertainty quantification framework for probabilistic model tuning that discovers model inputs distributions that generate target output distributions. Probabilistic sampling is performed using a surrogate model for computational efficiency, and a general distribution parameterization is used to describe each input. The approach is tested on seven patient-specific modeling examples using CircAdapt, a cardiovascular circulatory model. Six examples are synthetic, aiming to match the output distributions generated using known reference input data distributions, while the seventh example uses real-world patient data for the output distributions. Our results demonstrate the accurate reproduction of the target output distributions, with a correct recreation of the reference inputs for the six synthetic examples. Our proposed approach is suitable for determining the parameter distributions of patient-specific models with uncertain data and can be used to gain insights into the sensitivity of the model parameters to the measured data.

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Source
http://dx.doi.org/10.1002/cnm.3665DOI Listing

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