迈向不确定性意识溶血建模:解决实验方差的通用方法

IF 2.2 4区 医学 Q3 ENGINEERING, BIOMEDICAL
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,&nbsp;Ulrich Steinseifer,&nbsp;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,&nbsp;Ulrich Steinseifer,&nbsp;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}
引用次数: 0

摘要

本研究的目的是解决数值溶血模型中不确定度量化的缺乏,这对医疗器械评估至关重要。具体来说,我们的目标是开发一个概率溶血模型,该模型使用马尔可夫链蒙特卡罗(MCMC)方法结合实验可变性来提高预测精度和鲁棒性。首先,我们通过采用网格搜索方法,检查了拟合幂律溶血模型的目标函数景观,该模型的参数来自固有的不确定实验数据。在此基础上,我们应用MCMC推导出溶血幂律模型参数C、α和β的详细随机分布。然后通过FDA基准泵的降阶模型传播这些分布,以量化溶血测量中相对于预测的泵溶血的实验不确定性。我们的分析揭示了多参数幂律模型的目标函数景观的全局平坦最小值,这一现象可归因于拟合过程中的基本数学限制。概率溶血模型收敛于常数最优C = 3.515 × 10−5和α和β的对数正态分布,均值分别为0.614和1.795。这种概率方法成功地捕获了在实验FDA基准泵数据中观察到的平均值和方差。相比之下,传统的确定性模型不能描述实验变化。通过MCMC纳入不确定度量化,增强了溶血模型的稳健性和预测准确性。该方法可以更好地将模拟溶血结果与体外实验进行比较,并可以整合其他数据集,有可能为溶血建模设定新的标准。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
International Journal for Numerical Methods in Biomedical Engineering
International Journal for Numerical Methods in Biomedical Engineering ENGINEERING, BIOMEDICAL-MATHEMATICAL & COMPUTATIONAL BIOLOGY
CiteScore
4.50
自引率
9.50%
发文量
103
审稿时长
3 months
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.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信