Analysis of eligibility criteria complexity in clinical trials.

Jessica Ross, Samson Tu, Simona Carini, Ida Sim
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Abstract

Formal, computer-interpretable representations of eligibility criteria would allow computers to better support key clinical research and care use cases such as eligibility determination. To inform the development of such formal representations for eligibility criteria, we conducted this study to characterize and quantify the complexity present in 1000 eligibility criteria randomly selected from studies in ClinicalTrials.gov. We classified the criteria by their complexity, semantic patterns, clinical content, and data sources. Our analyses revealed significant semantic and clinical content variability. We found that 93% of criteria were comprehensible, with 85% of these criteria having significant semantic complexity, including 40% relying on temporal data. We also identified several domains of clinical content. Using the findings of the study as requirements for computer-interpretable representations of eligibility, we discuss the challenges for creating such representations for use in clinical research and practice.

临床试验资格标准复杂性分析。
正式的、计算机可解释的资格标准表示将允许计算机更好地支持关键的临床研究和护理用例,例如资格确定。为了向合格标准的正式表述的发展提供信息,我们进行了这项研究,以表征和量化从ClinicalTrials.gov的研究中随机选择的1000个合格标准中存在的复杂性。我们根据其复杂性、语义模式、临床内容和数据源对标准进行分类。我们的分析揭示了显著的语义和临床内容可变性。我们发现93%的标准是可理解的,其中85%的标准具有显著的语义复杂性,其中40%依赖于时间数据。我们还确定了临床内容的几个领域。使用研究结果作为计算机可解释的资格表示的要求,我们讨论了在临床研究和实践中创建这种表示的挑战。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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