从多模态电子病历中发现潜在因果源并量化其影响
A tutorial on discovering and quantifying the effect of latent causal sources of multimodal EHR data
- 通过概率独立分解，从不完整临床数据中提取潜在因果源
- 构建特定任务因果模型，可估计各来源对临床结果的影响
- 已在真实场景验证，适合医疗大数据的因果探索
我们提供了一个可复现的因果机器学习流程，用于（i）发现大规模电子健康记录中的潜在因果来源，（ii）量化这些来源对临床结局的影响。通过处理不完整的多模态临床数据，将其分解为概率独立的潜在源，并基于此训练特定任务的因果模型，从而估算个体因果效应。本文总结了该方法迄今两个真实世界应用的研究成果，展示了其在规模化医学发现中的通用性与实用性。
原文摘要 · Abstract (English)
We provide an accessible description of a peer-reviewed generalizable causal machine learning pipeline to (i) discover latent causal sources of large-scale electronic health records observations, and (ii) quantify the source causal effects on clinical outcomes. We illustrate how imperfect multimodal clinical data can be processed, decomposed into probabilistic independent latent sources, and used to train taskspecific causal models from which individual causal effects can be estimated. We summarize the findings of the two real-world applications of the approach to date as a demonstration of its versatility and utility for medical discovery at scale.
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