Understanding and Predicting Organ Donation Outcomes Using Network-based Predictive Analytics
Understanding and Predicting Organ Donation Outcomes Using Network-based Predictive Analytics
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使用基于网络的预测分析了解和预测器官捐赠结果
DOI：
10.1016/j.procs.2021.05.020
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发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Salih Tutun
中科院分区：
文献类型：
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作者：
E. Khan;Salih Tutun
移植所需的器官数量与可供捐赠的器官数量之间的差距越来越大。因此，每年有成千上万的人在等待器官移植时死亡。因此，现在比以往任何时候都更重要的是研究与器官捐赠相关的因素。更好地了解这些因素将有助于制定数据驱动的策略，以改善器官捐献的家庭同意。这项研究结合了机器学习方法和网络科学，以准确预测器官捐赠同意结果。在这项研究中，从纽约市的器官采购组织（OPO）获得了6年的患者数据，并用于提出同意预测模型。还对各种预测模型进行了比较。OPO现在可以使用最好的模型来制定优化同意率的策略，从而挽救更多的生命。实验结果表明，我们的方法在检测方面表现出色，因为我们结合了网络和机器学习算法，以获得更清晰的见解。建议的方法可以作为一个专家系统，以提高器官捐赠的同意率，从而弥补器官需求和供应之间的差距。
There is an ever-increasing disparity between the number of organs needed for transplantation and the number available for donation. As a result, thousands of people die every year while waiting for an organ transplant. Therefore, it is now more critical than ever to study the factors associated with organ donation. A better understanding of such factors will help immeasurably in formulating data-driven strategies for improving familial consent for organ donation. This research combines machine learning methods and network science to accurately predict organ donation consent outcomes. In this study, six years of patient data from an organ procurement organization (OPO) in New York City were obtained and used to propose the consent prediction model. A comparison of the various prediction models was also conducted. OPOs can now use the best models to develop strategies for optimizing the consent rate, thereby saving more lives. The experimental results show that our approach outperformed in terms of detection because we combined network and machine learning algorithms to obtain clearer insights. The proposed approach can be used as an expert system to increase the organ donation consent rate, thereby bridging the gap between organ demand and supply.