Demand-Driven Clustering in Relational Domains for Predicting Adverse Drug Events
Demand-Driven Clustering in Relational Domains for Predicting Adverse Drug Events
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发表时间：
2012-06
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通讯作者：
Jesse Davis;V. S. Costa;E. Berg;David Page;P. Peissig;Michael Caldwell
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作者：
Jesse Davis;V. S. Costa;E. Berg;David Page;P. Peissig;Michael Caldwell
从电子病历（EMR）中学习是具有挑战性的，因为它们的关系性质和患者过去和未来健康状态之间的不确定依赖性。统计关系学习是分析电子病历的天然选择，但不太擅长处理其固有的潜在结构，例如相关药物或疾病之间的联系。捕获潜在结构的一种方法是通过对象的关系聚类。我们提出了一种新的方法，而不是预先聚类的对象，在学习过程中进行需求驱动的聚类。我们在三个现实世界的任务中评估我们的算法，其目标是使用EMR来预测患者是否会对药物产生不良反应。我们发现，我们的方法是更准确的比不执行聚类，预聚类，并使用专家构建的医疗异质性。
Learning from electronic medical records (EMR) is challenging due to their relational nature and the uncertain dependence between a patient's past and future health status. Statistical relational learning is a natural fit for analyzing EMRs but is less adept at handling their inherent latent structure, such as connections between related medications or diseases. One way to capture the latent structure is via a relational clustering of objects. We propose a novel approach that, instead of pre-clustering the objects, performs a demand-driven clustering during learning. We evaluate our algorithm on three real-world tasks where the goal is to use EMRs to predict whether a patient will have an adverse reaction to a medication. We find that our approach is more accurate than performing no clustering, pre-clustering, and using expert-constructed medical heterarchies.