Oklahoma State University, Tulsa, 74106, USA.
Artif Intell Med. 2010 May;49(1):33-42. doi: 10.1016/j.artmed.2010.01.002. Epub 2010 Feb 13.
The prediction of survival time after organ transplantations and prognosis analysis of different risk groups of transplant patients are not only clinically important but also technically challenging. The current studies, which are mostly linear modeling-based statistical analyses, have focused on small sets of disparate predictive factors where many potentially important variables are neglected in their analyses. Data mining methods, such as machine learning-based approaches, are capable of providing an effective way of overcoming these limitations by utilizing sufficiently large data sets with many predictive factors to identify not only linear associations but also highly complex, non-linear relationships. Therefore, this study is aimed at exploring risk groups of thoracic recipients through machine learning-based methods.
A large, feature-rich, nation-wide thoracic transplantation dataset (obtained from the United Network for Organ Sharing-UNOS) is used to develop predictive models for the survival time estimation. The predictive factors that are most relevant to the survival time identified via, (1) conducting sensitivity analysis on models developed by the machine learning methods, (2) extraction of variables from the published literature, and (3) eliciting variables from the medical experts and other domain specific knowledge bases. A unified set of predictors is then used to develop a Cox regression model and the related prognosis indices. A comparison of clustering algorithm-based and conventional risk grouping techniques is conducted based on the outcome of the Cox regression model in order to identify optimal number of risk groups of thoracic recipients. Finally, the Kaplan-Meier survival analysis is performed to validate the discrimination among the identified various risk groups.
The machine learning models performed very effectively in predicting the survival time: the support vector machine model with a radial basis Kernel function produced the best fit with an R(2) value of 0.879, the artificial neural network (multilayer perceptron-MLP-model) came the second with an R(2) value of 0.847, and the M5 algorithm-based regression tree model came last with an R(2) value of 0.785. Following the proposed method, a consolidated set of predictive variables are determined and used to build the Cox survival model. Using the prognosis indices revealed by the Cox survival model along with a k-means clustering algorithm, an optimal number of "three" risk groups is identified. The significance of differences among these risk groups are also validated using the Kaplan-Meier survival analysis.
This study demonstrated that the integrated machine learning method to select the predictor variables is more effective in developing the Cox survival models than the traditional methods commonly found in the literature. The significant distinction among the risk groups of thoracic patients also validates the effectiveness of the methodology proposed herein. We anticipate that this study (and other AI based analytic studies like this one) will lead to more effective analyses of thoracic transplant procedures to better understand the prognosis of thoracic organ recipients. It would potentially lead to new medical and biological advances and more effective allocation policies in the field of organ transplantation.
器官移植后生存时间的预测和不同移植风险组的预后分析不仅具有临床重要性，而且具有技术挑战性。目前的研究主要基于线性建模的统计分析，侧重于小的、不同的预测因素集，在分析中忽略了许多潜在的重要变量。数据挖掘方法（如基于机器学习的方法）能够提供一种有效的方法，通过利用具有许多预测因素的足够大数据集，不仅可以识别线性关联，还可以识别高度复杂的非线性关系，从而克服这些限制。因此，本研究旨在通过基于机器学习的方法探索胸接受者的风险组。
使用大型、特征丰富的全国性胸移植数据集（从联合器官共享网络-UNOS 获得），通过机器学习方法开发预测模型，用于估计生存时间。通过（1）对机器学习方法开发的模型进行敏感性分析，（2）从已发表的文献中提取变量，以及（3）从医学专家和其他领域特定知识库中提取变量，确定与生存时间最相关的预测因素。然后使用统一的预测因子集开发 Cox 回归模型和相关的预后指标。基于 Cox 回归模型的结果，对聚类算法和传统风险分组技术进行比较，以确定胸接受者的最佳风险组数量。最后，进行 Kaplan-Meier 生存分析，以验证所确定的各种风险组之间的区分度。
机器学习模型在预测生存时间方面表现非常有效：具有径向基核函数的支持向量机模型拟合度最好，R(2)值为 0.879，人工神经网络（多层感知器-MLP 模型）次之，R(2)值为 0.847，基于 M5 算法的回归树模型最差，R(2)值为 0.785。按照提出的方法，确定了一组综合预测变量，并用于构建 Cox 生存模型。使用 Cox 生存模型揭示的预后指标和 k-means 聚类算法，确定了“三个”最佳风险组数量。还使用 Kaplan-Meier 生存分析验证了这些风险组之间差异的显著性。
本研究表明，与文献中常见的传统方法相比，集成机器学习方法选择预测变量更有效地开发 Cox 生存模型。胸患者风险组之间的显著差异也验证了本文提出的方法的有效性。我们预计，这项研究（和其他类似的基于人工智能的分析研究）将导致对胸移植手术的更有效的分析，从而更好地理解胸器官接受者的预后。它有可能带来新的医学和生物学进展，并在器官移植领域制定更有效的分配政策。
