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首页> 外文期刊>BMC Medical Informatics and Decision Making >Exploiting mutual information for the imputation of static and dynamic mixed-type clinical data with an adaptive k-nearest neighbours approach
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Exploiting mutual information for the imputation of static and dynamic mixed-type clinical data with an adaptive k-nearest neighbours approach

机译：利用自适应k-最近邻居方法利用静态和动态混合型临床数据的载体的互信

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Clinical registers constitute an invaluable resource in the medical data-driven decision making context. Accurate machine learning and data mining approaches on these data can lead to faster diagnosis, definition of tailored interventions, and improved outcome prediction. A typical issue when implementing such approaches is the almost unavoidable presence of missing values in the collected data. In this work, we propose an imputation algorithm based on a mutual information-weighted k-nearest neighbours approach, able to handle the simultaneous presence of missing information in different types of variables. We developed and validated the method on a clinical register, constituted by the information collected over subsequent screening visits of a cohort of patients affected by amyotrophic lateral sclerosis. For each subject with missing data to be imputed, we create a feature vector constituted by the information collected over his/her first three months of visits. This vector is used as sample in a k-nearest neighbours procedure, in order to select, among the other patients, the ones with the most similar temporal evolution of the disease over time. An ad hoc similarity metric was implemented for the sample comparison, capable of handling the different nature of the data, the presence of multiple missing values and include the cross-information among features captured by the mutual information statistic. We validated the proposed imputation method on an independent test set, comparing its performance with those of three state-of-the-art competitors, resulting in better performance. We further assessed the validity of our algorithm by comparing the performance of a survival classifier built on the data imputed with our method versus the one built on the data imputed with the best-performing competitor. Imputation of missing data is a crucial –and often mandatory– step when working with real-world datasets. The algorithm proposed in this work could effectively impute an amyotrophic lateral sclerosis clinical dataset, by handling the temporal and the mixed-type nature of the data and by exploiting the cross-information among features. We also showed how the imputation quality can affect a machine learning task.
机译：临床寄存器构成医疗数据驱动决策中的宝贵资源。这些数据的准确机器学习和数据挖掘方法可以导致更快的诊断，定制干预措施的定义和改善的结果预测。实现此类方法时的典型问题是收集数据中缺失值的几乎不可避免地存在。在这项工作中，我们提出了一种基于相互信息加权的K-Collecti邻邻居方法的估算算法，能够处理不同类型的变量中缺失信息的同时存在。我们在临床登记册上开发并验证了该方法，由收集的信息，随后筛选受肌萎缩侧面硬化的患者群组的综合筛查。对于具有缺失数据的每个主题，我们创建一个由在他/她前三个月内收集的信息构成的特征向量。该载体用作K-CORMALY邻居程序中的样品，以便在其他患者中选择具有最相似的疾病的时间逐渐发展的患者。为样本比较实现了Ad Hoc相似度指标，其能够处理数据的不同性质，存在多个缺失值的存在，并且包括由互信息统计捕获的特征之间的交叉信息。我们在独立的测试集中验证了拟议的撤销方法，将其与三个最先进的竞争对手的性能进行比较，从而提高性能。我们进一步通过比较了对由我们的方法所归发的数据的生存分类器的性能进行了评估了我们的算法的有效性，而不是基于最佳竞争对手的数据。缺失数据的归责是一个重要的 - 使用现实世界数据集时经常是强制性的。本作作品中提出的算法可以通过处理数据的时间和混合类型性质以及利用特征之间的交叉信息来有效地赋予肌营养的横向硬化临床数据集。我们还显示了估算质量如何影响机器学习任务。

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