Department of Computer Science and Numerical Analysis, University of Córdoba, Rabanales Campus, Albert Einstein Building 3rd Floor, 14071 Córdoba, Spain.
Artif Intell Med. 2013 May;58(1):37-49. doi: 10.1016/j.artmed.2013.02.004. Epub 2013 Mar 13.
The optimal allocation of organs in liver transplantation is a problem that can be resolved using machine-learning techniques. Classical methods of allocation included the assignment of an organ to the first patient on the waiting list without taking into account the characteristics of the donor and/or recipient. In this study, characteristics of the donor, recipient and transplant organ were used to determine graft survival. We utilised a dataset of liver transplants collected by eleven Spanish hospitals that provides data on the survival of patients three months after their operations.
To address the problem of organ allocation, the memetic Pareto evolutionary non-dominated sorting genetic algorithm 2 (MPENSGA2 algorithm), a multi-objective evolutionary algorithm, was used to train radial basis function neural networks, where accuracy was the measure used to evaluate model performance, along with the minimum sensitivity measurement. The neural network models obtained from the Pareto fronts were used to develop a rule-based system. This system will help medical experts allocate organs.
The models obtained with the MPENSGA2 algorithm generally yielded competitive results for all performance metrics considered in this work, namely the correct classification rate (C), minimum sensitivity (MS), area under the receiver operating characteristic curve (AUC), root mean squared error (RMSE) and Cohen's kappa (Kappa). In general, the multi-objective evolutionary algorithm demonstrated a better performance than the mono-objective algorithm, especially with regard to the MS extreme of the Pareto front, which yielded the best values of MS (48.98) and AUC (0.5659). The rule-based system efficiently complements the current allocation system (model for end-stage liver disease, MELD) based on the principles of efficiency and equity. This complementary effect occurred in 55% of the cases used in the simulation. The proposed rule-based system minimises the prediction probability error produced by two sets of models (one of them formed by models guided by one of the objectives (entropy) and the other composed of models guided by the other objective (MS)), such that it maximises the probability of success in liver transplants, with success based on graft survival three months post-transplant.
The proposed rule-based system is objective, because it does not involve medical experts (the expert's decision may be biased by several factors, such as his/her state of mind or familiarity with the patient). This system is a useful tool that aids medical experts in the allocation of organs; however, the final allocation decision must be made by an expert.
器官在肝移植中的最优分配是一个可以通过机器学习技术解决的问题。经典的分配方法包括将器官分配给等待名单上的第一位患者，而不考虑供体和/或受体的特征。在这项研究中，使用供体、受体和移植器官的特征来确定移植物的存活率。我们利用了由 11 家西班牙医院收集的肝移植数据集，该数据集提供了患者手术后三个月生存的数据。
为了解决器官分配问题，使用了多目标进化非支配排序遗传算法 2（MPENSGA2 算法），一种多目标进化算法，来训练径向基函数神经网络，其中准确性是评估模型性能的度量，同时还使用了最小灵敏度测量。从 Pareto 前沿获得的神经网络模型被用于开发基于规则的系统。这个系统将帮助医学专家分配器官。
MPENSGA2 算法获得的模型通常在这项工作中考虑的所有性能指标上都取得了有竞争力的结果，即正确分类率（C）、最小灵敏度（MS）、接收者操作特征曲线下的面积（AUC）、均方根误差（RMSE）和 Cohen 的 kappa（Kappa）。一般来说，多目标进化算法的性能优于单目标算法，特别是在 Pareto 前沿的 MS 极端情况下，其产生了最佳的 MS（48.98）和 AUC（0.5659）值。基于效率和公平性原则，基于规则的系统有效地补充了当前的分配系统（终末期肝病模型，MELD）。在模拟中使用的 55%的病例中出现了这种互补效应。所提出的基于规则的系统最小化了两组模型（一组由一个目标（熵）指导的模型组成，另一组由另一个目标（MS）指导的模型组成）产生的预测概率误差，从而使肝脏移植的成功率最大化，成功基于移植后三个月的移植物存活率。
所提出的基于规则的系统是客观的，因为它不涉及医学专家（专家的决策可能会受到多种因素的影响，例如他/她的心态或对患者的熟悉程度）。这个系统是一个有用的工具，可以帮助医学专家分配器官；然而，最终的分配决策必须由专家做出。
