基于梯度增强和适应性基因分配的公平高效器官移植数据驱动框架。

IF 1.3 4区 医学 Q4 ENGINEERING, BIOMEDICAL
Sangeetha Gnanasambandan, Vanathi Balasubramanian
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引用次数: 0

摘要

本研究提出了一个全面的数据驱动框架,旨在通过优化风险评估、供体-受体匹配和公平的器官分配来提高器官移植效率。该框架利用梯度增强算法(GBA)进行风险优先排序,A*搜索寻找最优供体位置,改进的基于卷积神经网络的混合极限学习分类器(MCNN-HELM)模型进行精确匹配,以及自适应目标加权遗传分配(AOWGA)算法,解决了器官分配和分布中的关键挑战。实验结果表明,集成系统的总体准确率为96%,分配效率为97%,公平性指数为0.92。MCNN-HELM模型的匹配精度为0.94,准确率为97.5%,优于现有方法。AOWGA优于比较分配方法,分配效率为0.96,阳性结果率为0.95。通过整合这些模块,该框架不仅改善了器官分配过程,还提高了存活率,并促进了器官分配的道德实践。通过创新地整合这些技术,该框架减少了等待时间,改善了患者的治疗效果,并确保了公平分配,标志着在解决持续的器官短缺问题方面取得了重大进展,并为道德和高效的器官移植树立了新的标准。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A data-driven framework for fair and efficient organ transplantation using gradient boosting and adaptive genetic allocation.

This study proposes a comprehensive data-driven framework aimed at enhancing organ transplantation efficiency through optimized risk assessment, donor-recipient matching, and equitable organ allocation. Utilizing the gradient boosting algorithm (GBA) for risk prioritization, A* search for optimal donor location, the modified convolutional neural network-based hybrid extreme learning classifier (MCNN-HELM) model for precise matching, and an adaptive objective-weighted genetic allocation (AOWGA) algorithm, the framework addresses critical challenges in organ allocation and distribution. The experimental results indicate strong performance metrics, with the integrated system achieving an overall accuracy of 96%, allocation efficiency of 97%, and a fairness index of 0.92. The MCNN-HELM model showed a matching precision of 0.94 and an accuracy of 97.5%, outperforming existing methods. AOWGA surpassed comparative allocation methods, demonstrating an allocation efficiency of 0.96 and a positive outcome rate of 0.95. By integrating these modules, the framework not only improves organ allocation processes but also enhances survival rates and promotes ethical practices in organ distribution. By innovatively integrating these techniques, the framework reduces waiting times, improves patient outcomes, and ensures fair allocation, marking a significant advancement in addressing the persistent organ shortage and setting a new standard for ethical and efficient organ transplantation.

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来源期刊
Journal of Artificial Organs
Journal of Artificial Organs 医学-工程:生物医学
CiteScore
2.80
自引率
15.40%
发文量
68
审稿时长
6-12 weeks
期刊介绍: The aim of the Journal of Artificial Organs is to introduce to colleagues worldwide a broad spectrum of important new achievements in the field of artificial organs, ranging from fundamental research to clinical applications. The scope of the Journal of Artificial Organs encompasses but is not restricted to blood purification, cardiovascular intervention, biomaterials, and artificial metabolic organs. Additionally, the journal will cover technical and industrial innovations. Membership in the Japanese Society for Artificial Organs is not a prerequisite for submission.
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