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首页> 外文期刊>BMC Medical Informatics and Decision Making >Bayesian network models with decision tree analysis for management of childhood malaria in Malawi
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Bayesian network models with decision tree analysis for management of childhood malaria in Malawi

机译：贝叶斯网络模型与童年疟疾管理中的决策树分析

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Malaria is a major cause of death in children under five years old in low- and middle-income countries such as Malawi. Accurate diagnosis and management of malaria can help reduce the global burden of childhood morbidity and mortality. Trained healthcare workers in rural health centers manage malaria with limited supplies of malarial diagnostic tests and drugs for treatment. A clinical decision support system that integrates predictive models to provide an accurate prediction of malaria based on clinical features could aid healthcare workers in the judicious use of testing and treatment. We developed Bayesian network (BN) models to predict the probability of malaria from clinical features and an illustrative decision tree to model the decision to use or not use a malaria rapid diagnostic test (mRDT). We developed two BN models to predict malaria from a dataset of outpatient encounters of children in Malawi. The first BN model was created manually with expert knowledge, and the second model was derived using an automated method. The performance of the BN models was compared to other statistical models on a range of performance metrics at multiple thresholds. We developed a decision tree that integrates predictions with the costs of mRDT and a course of recommended treatment. The manually created BN model achieved an area under the ROC curve (AUC) equal to 0.60 which was statistically significantly higher than the other models. At the optimal threshold for classification, the manual BN model had sensitivity and specificity of 0.74 and 0.42 respectively, and the automated BN model had sensitivity and specificity of 0.45 and 0.68 respectively. The balanced accuracy values were similar across all the models. Sensitivity analysis of the decision tree showed that for values of probability of malaria below 0.04 and above 0.40, the preferred decision that minimizes expected costs is not to perform mRDT. In resource-constrained settings, judicious use of mRDT is important. Predictive models in combination with decision analysis can provide personalized guidance on when to use mRDT in the management of childhood malaria. BN models can be efficiently derived from data to support clinical decision making.
机译：疟疾是在低收入和中等收入国家的5岁以下儿童死亡的主要原因，如马拉维。准确的诊断和管理疟疾可以帮助减少儿童发病率和死亡率的全球负担。农村健康中心的训练有素的医疗工作人员管理疟疾的疟疾诊断试验和治疗药物供应。临床决策支持系统，整合预测模型，以基于临床特征提供对疟疾的准确预测可以帮助医疗工作者在明智地使用测试和治疗方面。我们开发了贝叶斯网络（BN）模型，以预测疟疾从临床特征和说明性决策树的概率，以模拟使用或不使用疟疾快速诊断测试（MRDT）。我们开发了两种BN模型，以预测来自马拉维儿童的门诊数据集的疟疾。第一个BN模型是用专业知识手动创建的，并且使用自动方法导出第二种模型。将BN模型的性能与多个阈值下的一系列性能度量的统计模型进行比较。我们开发了一个决策树，将预测与MRDT的成本集成在一起，以及推荐治疗的课程。手动创建的BN模型在ROC曲线（AUC）下的区域等于0.60，其统计上显着高于其他模型。在分类的最佳阈值下，手册BN模型分别具有0.74和0.42的灵敏度和特异性，自动BN模型分别具有0.45和0.68的灵敏度和特异性。所有模型的平衡精度值都相似。决策树的敏感性分析表明，对于疟疾的概率值低于0.04且高于0.40，最小化预期成本的优选决定是不执行MRDT。在资源约束的设置中，MRDT的明智使用很重要。与决策分析相结合的预测模型可以为何时使用MRDT在童年疟疾管理中提供个性化指导。 BN模型可以有效地从数据源自数据以支持临床决策。

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