Institute for Health Informatics.
University of Texas Health Science Center, Houston, TX, USA.
AMIA Annu Symp Proc. 2021 Jan 25;2020:243-252. eCollection 2020.
Dietary supplements (DSs) have been widely used in the U.S. and evaluated in clinical trials as potential interventions for various diseases. However, many clinical trials face challenges in recruiting enough eligible patients in a timely fashion, causing delays or even early termination. Using electronic health records to find eligible patients who meet clinical trial eligibility criteria has been shown as a promising way to assess recruitment feasibility and accelerate the recruitment process. In this study, we analyzed the eligibility criteria of 100 randomly selected DS clinical trials and identified both computable and non-computable criteria. We mapped annotated entities to OMOP Common Data Model (CDM) with novel entities (e.g., DS). We also evaluated a deep learning model (Bi-LSTM-CRF) for extracting these entities on CLAMP platform, with an average F1 measure of 0.601. This study shows the feasibility of automatic parsing of the eligibility criteria following OMOP CDM for future cohort identification.
膳食补充剂（DSs）在美国被广泛应用，并在临床试验中作为治疗各种疾病的潜在干预措施进行评估。然而，许多临床试验在及时招募足够合格患者方面面临挑战，导致延迟甚至提前终止。使用电子健康记录来寻找符合临床试验资格标准的合格患者已被证明是评估招募可行性和加速招募过程的一种有前途的方法。在这项研究中，我们分析了 100 项随机选择的 DS 临床试验的资格标准，并确定了可计算和不可计算的标准。我们将注释实体映射到 OMOP 通用数据模型 (CDM)，其中包括新实体（例如 DS）。我们还在 CLAMP 平台上评估了一种用于提取这些实体的深度学习模型（Bi-LSTM-CRF），平均 F1 度量值为 0.601。这项研究表明，按照 OMOP CDM 自动解析资格标准用于未来队列识别是可行的。
