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Abstract：
The purpose of this paper is to systematically sort out and analyze the cutting-edge research on the eligibility criteria of clinical trials. Eligibility criteria are important prerequisites for the success of clinical trials. It directly affects the final results of the clinical trials. Inappropriate eligibility criteria will lead to insufficient recruitment, which is an important reason for the eventual failure of many clinical trials. We have investigated the research status of eligibility criteria for clinical trials on academic platforms such as arXiv and NIH. We have classified and sorted out all the papers we found, so that readers can understand the frontier research in this field. Eligibility criteria are the most important part of a clinical trial study. The ultimate goal of research in this field is to formulate more scientific and reasonable eligibility criteria and speed up the clinical trial process. The global research on the eligibility criteria of clinical trials is mainly divided into four main aspects: natural language processing, patient pre-screening, standard evaluation, and clinical trial query. Compared with the past, people are now using new technologies to study eligibility criteria from a new perspective (big data). In the research process, complex disease concepts, how to choose a suitable dataset, how to prove the validity and scientific of the research results, are challenges faced by researchers (especially for computer-related researchers). Future research will focus on the selection and improvement of artificial intelligence algorithms related to clinical trials and related practical applications such as databases, knowledge graphs, and dictionaries.
摘要：
本文旨在对临床试验合格标准的前沿研究进行系统梳理和分析。合格标准是临床试验成功的重要先决条件。它直接影响临床试验的最终结果。不适当的资格标准将导致招聘不足,这是许多临床试验最终失败的重要原因。我们调查了arXiv和NIH等学术平台上临床试验资格标准的研究现状。我们已经对我们找到的所有文件进行了分类和整理，以便读者了解该领域的前沿研究。资格标准是临床试验研究中最重要的部分。该领域研究的最终目标是制定更科学合理的合格标准，加快临床试验进程。全球对临床试验资格标准的研究主要分为四个主要方面:自然语言处理、患者预筛查，标准评价,和临床试验查询。与过去相比，人们现在正在使用新技术从新的角度(大数据)研究资格标准。在研究过程中,复杂的疾病概念，如何选择合适的数据集，如何证明研究成果的有效性和科学性，是研究人员(尤其是计算机相关研究人员)面临的挑战。未来的研究将集中在与临床试验和数据库等相关实际应用相关的人工智能算法的选择和改进上,知识图谱，和字典。
