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首页> 外文会议>International Conference on Collaborative Computing: Networking, Applications and Worksharing >Domain ontology-based feature reduction for high dimensional drug data and its application to 30-day heart failure readmission prediction
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Domain ontology-based feature reduction for high dimensional drug data and its application to 30-day heart failure readmission prediction

机译：高维药物数据的基于领域本体的特征约简及其在心衰30天再入院预测中的应用

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摘要

High dimensional feature space could potentially hinder the efficiency and performance for machine learning, and high correlations between features may further increase the redundancy and diminish performance of learning algorithms. Domain ontology provides relationships and similarities between concepts in the specific area, and thus can be used to reduce redundancy by clustering concepts and revealing their functionality. In this paper, we study the problem of using high dimensional medication data to predict the probability of 30-Day heart failure readmission. We propose a feature reduction method for high dimensional dataset using a combination of two drug ontologies. By creating a tree structure of the combination, the method uses a greedy strategy to obtain a subset of features, which may have higher correlation with the class label but lower correlation with each other. Experimental results show that our methods improve the performance of heart failure readmission prediction (using only drug data) comparing to existing feature reduction methods without drug domain ontologies.
机译：高维特征空间可能会阻碍机器学习的效率和性能，而特征之间的高度相关性可能会进一步增加冗余度并降低学习算法的性能。领域本体提供特定区域中概念之间的关系和相似性，因此可以通过对概念进行聚类并揭示其功能来减少冗余。在本文中，我们研究了使用高维药物数据预测30天心力衰竭再次入院的可能性的问题。我们提出了一种结合了两种药物本体的高维数据集特征约简方法。通过创建组合的树结构，该方法使用贪婪策略来获取特征子集，这些特征子集与类标签的相关性可能较高，而彼此之间的相关性则较低。实验结果表明，与没有药物领域本体的现有特征减少方法相比，我们的方法提高了心力衰竭再入院预测的性能（仅使用药物数据）。

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