论文标题
Sparge：通过低排名约束和图形嵌入的稀疏基于编码的患者相似性学习
SparGE: Sparse Coding-based Patient Similarity Learning via Low-rank Constraints and Graph Embedding
论文作者
论文摘要
患者的相似性评估（PSA）对于循证基于证据和个性化医学至关重要，通过分析日益获得的电子健康记录（EHR）来实现。但是，PSA的机器学习方法必须处理EHR的固有数据缺陷，即缺少值，噪声和小样本量。在这项工作中，提出了一个称为Sparge的端到端判别学习框架，以应对PSA EHR的这些数据挑战。 Sparge通过共同稀疏编码和图形嵌入来衡量相似性。首先，我们使用低级约束的稀疏编码来识别和计算类似患者的体重，同时对缺失值降解。然后，采用嵌入在稀疏表示上的图，以通过保留按距离定义的局部关系来衡量患者对之间的相似性。最后，构建了一个全局成本函数以优化相关参数。对两个私人和公共现实世界中的医疗保健数据集的实验结果，即Singheart和Mimic-III，表明拟议的Sparge明显胜过其他机器学习患者的相似性方法。
Patient similarity assessment (PSA) is pivotal to evidence-based and personalized medicine, enabled by analyzing the increasingly available electronic health records (EHRs). However, machine learning approaches for PSA has to deal with inherent data deficiencies of EHRs, namely missing values, noise, and small sample sizes. In this work, an end-to-end discriminative learning framework, called SparGE, is proposed to address these data challenges of EHR for PSA. SparGE measures similarity by jointly sparse coding and graph embedding. First, we use low-rank constrained sparse coding to identify and calculate weight for similar patients, while denoising against missing values. Then, graph embedding on sparse representations is adopted to measure the similarity between patient pairs via preserving local relationships defined by distances. Finally, a global cost function is constructed to optimize related parameters. Experimental results on two private and public real-world healthcare datasets, namely SingHEART and MIMIC-III, show that the proposed SparGE significantly outperforms other machine learning patient similarity methods.
