Sensitivity analysis and optimization of a cardiovascular lumped parameter model for patient-specific modelling
Parameter estimation poses a significant challenge in developing patient-specific cardiovascular models. This study presents a framework that enhances parameter estimation in lumped parameter cardiovascular models by combining sensitivity analysis for parameter selection with multi-objective genetic...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | en |
| Published: |
Taylor and Francis Ltd.
2025
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/45299/1/Sensitivity%20analysis%20and%20optimization%20of%20a%20cardiovascular%20lumped.pdf http://umpir.ump.edu.my/id/eprint/45299/ https://doi.org/10.1080/10255842.2025.2525980 |
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| Summary: | Parameter estimation poses a significant challenge in developing patient-specific cardiovascular models. This study presents a framework that enhances parameter estimation in lumped parameter cardiovascular models by combining sensitivity analysis for parameter selection with multi-objective genetic algorithm optimization. Four key parameters were identified as the most influential and subsequently optimized. Model outputs, specifically mean arterial pressure (MAP), were validated against clinical values from a public database. The optimized model’s MAP demonstrated a strong correlation with clinical MAP (r = 0.99997, p < 0.001), and a t-test analysis (p = 0.752) confirmed statistical equivalence with clinical data. This approach highlights the potential of sensitivity analysis and genetic algorithms to improve accuracy in patient-specific cardiovascular modelling. |
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