Conway Mike, Berg Richard L, Carrell David, Denny Joshua C, Kho Abel N, Kullo Iftikhar J, Linneman James G, Pacheco Jennifer A, Peissig Peggy, Rasmussen Luke, Weston Noah, Chute Christopher G, Pathak Jyotishman
Mayo Clinic, Rochester, MN, USA.
AMIA Annu Symp Proc. 2011;2011:274-83. Epub 2011 Oct 22.
The need for formal representations of eligibility criteria for clinical trials - and for phenotyping more generally - has been recognized for some time. Indeed, the availability of a formal computable representation that adequately reflects the types of data and logic evidenced in trial designs is a prerequisite for the automatic identification of study-eligible patients from Electronic Health Records. As part of the wider process of representation development, this paper reports on an analysis of fourteen Electronic Health Record oriented phenotyping algorithms (developed as part of the eMERGE project) in terms of their constituent data elements, types of logic used and temporal characteristics. We discovered that the majority of eMERGE algorithms analyzed include complex, nested boolean logic and negation, with several dependent on cardinality constraints and complex temporal logic. Insights gained from the study will be used to augment the CDISC Protocol Representation Model.
一段时间以来，人们已经认识到需要对临床试验的入选标准进行形式化表示——更广泛地说，是对表型进行形式化表示。实际上，拥有一个能够充分反映试验设计中所体现的数据类型和逻辑的形式化可计算表示，是从电子健康记录中自动识别符合研究条件患者的先决条件。作为表示法开发这一更广泛过程的一部分，本文报告了对十四种面向电子健康记录的表型算法（作为eMERGE项目的一部分开发）在其组成数据元素、所使用的逻辑类型和时间特征方面的分析。我们发现，所分析的大多数eMERGE算法都包括复杂的嵌套布尔逻辑和否定，其中一些依赖于基数约束和复杂的时间逻辑。从该研究中获得的见解将用于扩充CDISC协议表示模型。
