一个数据驱动的框架,用于公平和高效的器官移植,使用梯度增强和适应性遗传分配
Sangeetha Gnanasambandan1, Vanathi Balasubramanian2
1Department of Computer Science and Engineering, SRM Valliammai Engineering College, Kattankulathur, Chennai, Tamil Nadu, India. sangeethagnanasambandan.mail@gmail.com.
概括
这项研究引入了一个数据驱动的框架,以提高器官移植的效率,使用先进的算法进行风险评估,供体与受体匹配和分配. 该系统显著提高了器官分配的准确性,效率和公平性,改善了患者的治疗结果.
科学领域:
- 医疗信息学 医疗信息学
- 生物医学工程 生物医学工程
- 计算生物学 计算生物学
背景情况:
- 器官移植在效率,风险评估,供体与受体匹配以及公平分配方面面临挑战.
- 由于分配效率低下,当前的系统经常面临长时间的等待和低于最佳的患者结果.
研究的目的:
- 开发和评估一个全面的数据驱动框架,以优化器官移植过程.
- 提高器官分配和分配的效率,准确性和公平性.
主要方法:
- 使用梯度提升算法 (GBA) 进行风险优先排序.
- 雇佣的A*寻找最佳的供体位置.
- 实现了基于卷积神经网络的改进混合极端学习分类器 (MCNN-HELM) 进行精确匹配.
- 开发了一种适应性客观加权基因分配 (AOWGA) 算法,以实现公平分配.
主要成果:
- 综合框架实现了96%的整体准确性和97%的分配效率.
- 在MCNN-HELM模型中,匹配精度达到了94%,准确度达到了97.5%.
- AOWGA算法显示了0.96的分配效率和0.95的积极结果率.
- 该系统实现了0.92.9的公平指数.
结论:
- 拟议的框架显著改善了器官分配过程,减少了等待时间,并提高了患者的生存率.
- 整合了GBA,A*搜索,MCNN-HELM和AOWGA,为伦理和高效的器官移植制定了新的标准.
- 这种数据驱动的方法解决了器官短缺问题,并促进了公平的分配,从而改善了患者的治疗结果.
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