在基于电子健康记录的可计算表型算法中使用二元银标签

中文摘要 AI 辅助

在电子健康记录（EHR）研究中，金标准表型标签往往因需人工病历审查而难以大规模获取。弱监督表型分析方法使用银标准标签，如诊断代码计数、自然语言处理提及、用药指标或实验室阈值。PheNorm广泛用于此，但原始公式适用于计数值银标签，依赖对数变换、利用率归一化和高斯混合建模，不适用于常见且信息丰富的二元银标签。我们提出二元PheNorm，在损坏和回归去噪步骤中直接使用二元银标签，无需EM校准即可产生连续表型评分。还考虑了高维EHR设置的套索正则化版本以及使用二元和计数标签的组合模型。模拟中，二元PheNorm仅用二元标签就实现了强区分，与计数标签组合时性能常提升。如过敏反应中，肾上腺素提及指标的AUC从0.793升至0.891 - 0.892；急性胰腺炎中，脂肪酶阈值指标的AUC从0.736升至0.805 - 0.819。结果支持二元PheNorm作为实用的弱监督方法。

英文摘要

Gold-standard phenotype labels are often unavailable at scale in electronic health record (EHR) studies because they require manual chart review. Weakly supervised phenotyping methods instead use silver-standard labels, such as diagnosis-code counts, natural language processing (NLP) mentions, medication indicators, or laboratory thresholds. PheNorm is widely used for this purpose, but its original formulation was designed for count-valued silver labels and relies on log transformation, utilization normalization, and Gaussian mixture modeling. These steps are not directly suited to binary silver labels, which are common and may be highly informative. We propose Binary PheNorm, an extension that uses binary silver labels directly in the corruption-and-regression denoising step and produces a continuous phenotype score without EM calibration. We also consider a lasso-regularized version for high-dimensional EHR settings and combined models using both binary and count labels. In simulations, Binary PheNorm achieved strong discrimination using binary labels alone and often improved performance when combined with count labels. In anaphylaxis, AUC increased from 0.793 for an epinephrine-mention indicator to 0.891-0.892 after Binary PheNorm. In acute pancreatitis, AUC increased from 0.736 for a lipase-threshold indicator to 0.805-0.819. These results support Binary PheNorm as a practical weakly supervised approach when informative binary silver labels are available.

发表机构

• Department of Biostatistics, University of Washington（华盛顿大学生物统计学系）
• Kaiser Permanente Center for Health Research（凯撒医疗研究中心）
• Biostatistics Division, Kaiser Permanente Washington Health Research Institute（凯撒医疗华盛顿健康研究机构生物统计学部）
• Kaiser Permanente Washington Health Research Institute（凯撒医疗华盛顿健康研究机构）
• Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center（弗雷德 Hutchinson 癌症中心疫苗与传染病部）

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