论文标题
表示医学数据的代表性学习
Representation Learning for Medical Data
论文作者
论文摘要
我们提出了一个医学诊断领域的表示学习框架。它基于基于异构网络的诊断数据模型以及用于学习潜在节点表示的修改的Metapath2VEC算法。我们将所提出的算法与两个实际案例研究中的其他表示学习方法进行了比较：症状/疾病分类和疾病预测。我们观察到以异质网络形式的学习表示形式导致的这些任务的显着提高。
We propose a representation learning framework for medical diagnosis domain. It is based on heterogeneous network-based model of diagnostic data as well as modified metapath2vec algorithm for learning latent node representation. We compare the proposed algorithm with other representation learning methods in two practical case studies: symptom/disease classification and disease prediction. We observe a significant performance boost in these task resulting from learning representations of domain data in a form of heterogeneous network.
