A major bottleneck hindering utilization of electronic health record data for translational research is the lack of precise phenotype labels. Chart review as well as rule-based and supervised phenotyping approaches require laborious expert input, hampering applicability to studies that require many phenotypes to be defined and labeled de novo. Though International Classification of Diseases codes are often used as surrogates for true labels in this setting, these sometimes suffer from poor specificity. We propose a fully automated topic modeling algorithm to simultaneously annotate multiple phenotypes.

中文翻译：

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sureLDA：一种用于电子健康记录的多疾病自动表型分析方法。

阻碍将电子健康记录数据用于转化研究的一个主要瓶颈是缺乏精确的表型标签。图表审查以及基于规则和监督的表型分析方法需要费力的专家投入，这阻碍了需要从头定义和标记许多表型的研究的适用性。尽管在这种情况下，国际疾病分类代码经常被用作真实标签的替代品，但这些代码有时特异性较差。我们提出了一种全自动的主题建模算法来同时注释多个表型。