寻找合适的临床试验：评估数据不完整时的编码入选标准。
Finding appropriate clinical trials: evaluating encoded eligibility criteria with incomplete data.
作者信息
Ash N, Ogunyemi O, Zeng Q, Ohno-Machado L
机构信息
Decision Systems Group, Brigham and Woman's Hospital, Harvard Medical School, Boston, MA, USA.
出版信息
Proc AMIA Symp. 2001:27-31.
We describe our work on creating a system that selects appropriate clinical trials by automating the evaluation of eligibility criteria. We developed a data model of eligibility for breast cancer clinical trials, upon which the criteria were encoded. Standard vocabularies are utilized to represent concepts used in the system, and retrieve their hierarchical relationships. The system incorporates Bayesian networks to handle missing patient information. Protocols are ranked by the belief that the patient is eligible for each of them. In a preliminary evaluation, we found good agreement (kappa 0.86) between the system and an independent physician in selection of protocols, but poor agreement (kappa 0.24) in protocol ranking. We conclude that our approach is feasible, and potentially useful in assisting both physicians and patients in the task of selecting appropriate trials.
我们描述了关于创建一个通过自动评估入选标准来选择合适临床试验的系统的工作。我们开发了一个乳腺癌临床试验入选资格的数据模型，入选标准在该模型上进行编码。使用标准词汇表来表示系统中使用的概念，并检索它们的层次关系。该系统纳入了贝叶斯网络来处理缺失的患者信息。通过患者符合每个方案的可能性对方案进行排序。在初步评估中，我们发现该系统与一名独立医生在方案选择上有良好的一致性（kappa值为0.86），但在方案排序上一致性较差（kappa值为0.24）。我们得出结论，我们的方法是可行的，并且在协助医生和患者选择合适试验的任务中可能会有用。
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