Das Trisha, Shafquat Afrah, Beigi Mandis, Aptekar Jacob, Mezey Jason, Sun Jimeng
University of Illinois Urbana-Champaign, Urbana, IL.
Medidata, New York, NY.
AMIA Annu Symp Proc. 2025 May 22;2024:329-338. eCollection 2024.
Clinical trial data used to evaluate new treatments have value beyond the original studies, but limitations in data access due to privacy concerns make further use of these data challenging. Digital twins offer a solution by simulating patient outcomes, providing less restricted data access, reducing costs and increasing sample sizes. However, existing research focuses on synthetic Electronic Healthcare Records (EHRs) and lacks personalized patient record generation. This paper introduces SeqTrial, a framework for generating personalized digital twins for sequential clinical trial event data. The method uses BioBERT word embeddings to capture biomedical term semantics, an attention mechanism to understand visit relationships, and synthesizes digital twins for each patient. SeqTrial generates utility-preserving digital twins capable of estimating clinical outcomes, while addressing data scarcity through self-supervised pretraining. The method demonstrates high fidelity and utility in generating synthetic sequential clinical trial data for patient outcome prediction while ensuring privacy protection. The code is available at.
用于评估新疗法的临床试验数据具有超出原始研究的价值，但由于隐私问题导致的数据访问限制使得进一步利用这些数据具有挑战性。数字孪生通过模拟患者预后提供了一种解决方案，可提供限制较少的数据访问、降低成本并增加样本量。然而，现有研究集中在合成电子健康记录（EHR）上，缺乏个性化患者记录生成。本文介绍了SeqTrial，这是一个为序贯临床试验事件数据生成个性化数字孪生的框架。该方法使用BioBERT词嵌入来捕捉生物医学术语语义，使用注意力机制来理解就诊关系，并为每个患者合成数字孪生。SeqTrial生成能够估计临床预后的保留效用的数字孪生，同时通过自监督预训练解决数据稀缺问题。该方法在生成用于患者预后预测的合成序贯临床试验数据时展示了高保真度和效用，同时确保了隐私保护。代码可在……获取。
