Christopher Blum, Ulrich Steinseifer, Michael Neidlin
{"title":"迈向不确定性意识溶血建模:解决实验方差的通用方法","authors":"Christopher Blum, Ulrich Steinseifer, Michael Neidlin","doi":"10.1002/cnm.70040","DOIUrl":null,"url":null,"abstract":"<p>The purpose of this study is to address the lack of uncertainty quantification in numerical hemolysis models, which are critical for medical device evaluations. Specifically, we aim to develop a probabilistic hemolysis model, which incorporates experimental variability using the Markov Chain Monte Carlo (MCMC) method to enhance predictive accuracy and robustness. Initially, we examined the objective function landscape for fitting a Power Law hemolysis model, whose parameters are derived from inherently uncertain experimental data, by employing a grid search approach. Building on this, we applied MCMC to derive detailed stochastic distributions for the hemolysis Power Law model parameters <i>C</i>, <i>α</i>, and <i>β</i>. These distributions were then propagated through a reduced order model of the FDA benchmark pump to quantify the experimental uncertainty in hemolysis measurements with respect to the predicted pump hemolysis. Our analysis revealed a global flat minimum in the objective function landscape of the multi-parameter power law model, a phenomenon attributable to fundamental mathematical limitations in the fitting process. The probabilistic hemolysis model converged to a constant optimal <i>C</i> = 3.515 × 10<sup>−5</sup> and log normal distributions of <i>α</i> and <i>β</i> with means of 0.614 and 1.795, respectively. This probabilistic approach successfully captured both the mean and variance observed in the experimental FDA benchmark pump data. In comparison, conventional deterministic models are not able to describe experimental variation. Incorporating uncertainty quantification through MCMC enhances the robustness and predictive accuracy of hemolysis models. This method allows for better comparison of simulated hemolysis outcomes with in vitro experiments and can integrate additional datasets, potentially setting a new standard in hemolysis modeling.</p>","PeriodicalId":50349,"journal":{"name":"International Journal for Numerical Methods in Biomedical Engineering","volume":"41 5","pages":""},"PeriodicalIF":2.2000,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cnm.70040","citationCount":"0","resultStr":"{\"title\":\"Toward Uncertainty-Aware Hemolysis Modeling: A Universal Approach to Address Experimental Variance\",\"authors\":\"Christopher Blum, Ulrich Steinseifer, Michael Neidlin\",\"doi\":\"10.1002/cnm.70040\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>The purpose of this study is to address the lack of uncertainty quantification in numerical hemolysis models, which are critical for medical device evaluations. Specifically, we aim to develop a probabilistic hemolysis model, which incorporates experimental variability using the Markov Chain Monte Carlo (MCMC) method to enhance predictive accuracy and robustness. Initially, we examined the objective function landscape for fitting a Power Law hemolysis model, whose parameters are derived from inherently uncertain experimental data, by employing a grid search approach. Building on this, we applied MCMC to derive detailed stochastic distributions for the hemolysis Power Law model parameters <i>C</i>, <i>α</i>, and <i>β</i>. These distributions were then propagated through a reduced order model of the FDA benchmark pump to quantify the experimental uncertainty in hemolysis measurements with respect to the predicted pump hemolysis. Our analysis revealed a global flat minimum in the objective function landscape of the multi-parameter power law model, a phenomenon attributable to fundamental mathematical limitations in the fitting process. The probabilistic hemolysis model converged to a constant optimal <i>C</i> = 3.515 × 10<sup>−5</sup> and log normal distributions of <i>α</i> and <i>β</i> with means of 0.614 and 1.795, respectively. This probabilistic approach successfully captured both the mean and variance observed in the experimental FDA benchmark pump data. In comparison, conventional deterministic models are not able to describe experimental variation. Incorporating uncertainty quantification through MCMC enhances the robustness and predictive accuracy of hemolysis models. This method allows for better comparison of simulated hemolysis outcomes with in vitro experiments and can integrate additional datasets, potentially setting a new standard in hemolysis modeling.</p>\",\"PeriodicalId\":50349,\"journal\":{\"name\":\"International Journal for Numerical Methods in Biomedical Engineering\",\"volume\":\"41 5\",\"pages\":\"\"},\"PeriodicalIF\":2.2000,\"publicationDate\":\"2025-05-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cnm.70040\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal for Numerical Methods in Biomedical Engineering\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/cnm.70040\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ENGINEERING, BIOMEDICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal for Numerical Methods in Biomedical Engineering","FirstCategoryId":"5","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/cnm.70040","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, BIOMEDICAL","Score":null,"Total":0}
Toward Uncertainty-Aware Hemolysis Modeling: A Universal Approach to Address Experimental Variance
The purpose of this study is to address the lack of uncertainty quantification in numerical hemolysis models, which are critical for medical device evaluations. Specifically, we aim to develop a probabilistic hemolysis model, which incorporates experimental variability using the Markov Chain Monte Carlo (MCMC) method to enhance predictive accuracy and robustness. Initially, we examined the objective function landscape for fitting a Power Law hemolysis model, whose parameters are derived from inherently uncertain experimental data, by employing a grid search approach. Building on this, we applied MCMC to derive detailed stochastic distributions for the hemolysis Power Law model parameters C, α, and β. These distributions were then propagated through a reduced order model of the FDA benchmark pump to quantify the experimental uncertainty in hemolysis measurements with respect to the predicted pump hemolysis. Our analysis revealed a global flat minimum in the objective function landscape of the multi-parameter power law model, a phenomenon attributable to fundamental mathematical limitations in the fitting process. The probabilistic hemolysis model converged to a constant optimal C = 3.515 × 10−5 and log normal distributions of α and β with means of 0.614 and 1.795, respectively. This probabilistic approach successfully captured both the mean and variance observed in the experimental FDA benchmark pump data. In comparison, conventional deterministic models are not able to describe experimental variation. Incorporating uncertainty quantification through MCMC enhances the robustness and predictive accuracy of hemolysis models. This method allows for better comparison of simulated hemolysis outcomes with in vitro experiments and can integrate additional datasets, potentially setting a new standard in hemolysis modeling.
期刊介绍:
All differential equation based models for biomedical applications and their novel solutions (using either established numerical methods such as finite difference, finite element and finite volume methods or new numerical methods) are within the scope of this journal. Manuscripts with experimental and analytical themes are also welcome if a component of the paper deals with numerical methods. Special cases that may not involve differential equations such as image processing, meshing and artificial intelligence are within the scope. Any research that is broadly linked to the wellbeing of the human body, either directly or indirectly, is also within the scope of this journal.