从电子病历中挖掘系统性红斑狼疮的潜在病因异质性。
A data-driven approach to discover and quantify systemic lupus erythematosus etiological heterogeneity from electronic health records
- 基于多模态病历数据，发现19个独立的潜在病因源。
- 这些病因源在判别红斑狼疮时准确率高，具有临床有效性。
- 提升模型可解释性，帮助医生理解诊断依据。
系统性红斑狼疮（SLE）是一种表现多样、病因复杂的异质性疾病。本文提出一种数据驱动方法，从多模态不完整电子健康记录（EHR）中发现概率性独立来源。这些来源代表数据生成过程因果图中的外生变量，用于估计健康记录中SLE存在的潜在根本原因。通过在标注样本集上训练分类模型，以减少变量数量的方式客观评估这些来源的性能。结果发现19个具有高临床有效性的预测来源，其EHR特征定义了SLE异质性的独立因素。将这些来源作为患者表征输入模型，可提供丰富解释，更好捕捉某条记录是否为SLE病例的临床依据。在复杂病例中，临床医生可能愿意以个体可解释性换取更高判别能力。
原文摘要 · Abstract (English)
Systemic lupus erythematosus (SLE) is a complex heterogeneous disease with many manifestational facets. We propose a data-driven approach to discover probabilistic independent sources from multimodal imperfect EHR data. These sources represent exogenous variables in the data generation process causal graph that estimate latent root causes of the presence of SLE in the health record. We objectively evaluated the sources against the original variables from which they were discovered by training supervised models to discriminate SLE from negative health records using a reduced set of labelled instances. We found 19 predictive sources with high clinical validity and whose EHR signatures define independent factors of SLE heterogeneity. Using the sources as input patient data representation enables models to provide with rich explanations that better capture the clinical reasons why a particular record is (not) an SLE case. Providers may be willing to trade patient-level interpretability for discrimination especially in challenging cases.
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