PIVETed-Granite：通过约束张量分解计算表型
PIVETed-Granite: Computational Phenotypes through Constrained Tensor Factorization
摘要：
最近已经表明，多模态电子健康记录数据的稀疏非负张量因子分解是高通量计算表型的一种有前途的方法。然而，这样的方法在提取表型时通常不利用可用的领域知识;因此，一些建议的表型可能不能很好地映射到临床概念，或者可能与其他建议的表型非常相似。为了解决这些问题，我们提出了一种新的，自动的方法，称为PIVETed-Granite，挖掘现有的生物医学文献（PubMed），以获得不能链接的约束，然后在基于张量因子分解的计算表型分析过程中作为边信息。在使用来自VUMC的大型数据集来鉴定高血压患者表型的实验中，清楚地观察到了由此产生的改善。
英文摘要：
It has been recently shown that sparse, nonnegative tensor factorization of multi-modal electronic health record data is a promising approach to high-throughput computational phenotyping. However, such approaches typically do not leverage available domain knowledge while extracting the phenotypes; hence, some of the suggested phenotypes may not map well to clinical concepts or may be very similar to other suggested phenotypes. To address these issues, we present a novel, automatic approach called PIVETed-Granite that mines existing biomedical literature (PubMed) to obtain cannot-link constraints that are then used as side-information during a tensor-factorization based computational phenotyping process. The resulting improvements are clearly observed in experiments using a large dataset from VUMC to identify phenotypes for hypertensive patients.
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