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基于领域专用语言翻译的计算机辅助临床试验招募:以早产儿视网膜病变为例。

Computer-Aided Clinical Trial Recruitment Based on Domain-Specific Language Translation: A Case Study of Retinopathy of Prematurity.

机构信息

School of Management and E-Business, Zhejiang Gongshang University, Hangzhou, Zhejiang 310018, China.

Pediatric Retinal Surgery Department, Shenzhen Eye Hospital, Shenzhen Key Ophthalmic Laboratory, The Second Affiliated Hospital of Jinan University, Shenzhen, Guangzhou 518040, China.

出版信息

J Healthc Eng. 2017;2017:7862672. doi: 10.1155/2017/7862672. Epub 2017 Apr 5.

DOI:10.1155/2017/7862672
PMID:29065644
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5396472/
Abstract

Reusing the data from healthcare information systems can effectively facilitate clinical trials (CTs). How to select candidate patients eligible for CT recruitment criteria is a central task. Related work either depends on DBA (database administrator) to convert the recruitment criteria to native SQL queries or involves the data mapping between a standard ontology/information model and individual data source schema. This paper proposes an alternative computer-aided CT recruitment paradigm, based on syntax translation between different DSLs (domain-specific languages). In this paradigm, the CT recruitment criteria are first formally represented as production rules. The referenced rule variables are all from the underlying database schema. Then the production rule is translated to an intermediate query-oriented DSL (e.g., LINQ). Finally, the intermediate DSL is directly mapped to native database queries (e.g., SQL) automated by ORM (object-relational mapping).

摘要

复用医疗信息系统中的数据可以有效地促进临床试验 (CT)。如何选择符合 CT 招募标准的候选患者是一项核心任务。相关工作要么依赖于 DBA(数据库管理员)将招募标准转换为原生 SQL 查询,要么涉及标准本体/信息模型与各个数据源模式之间的数据映射。本文提出了一种基于不同 DSL(领域特定语言)之间的语法转换的替代计算机辅助 CT 招募范例。在此范例中,CT 招募标准首先被正式表示为产生式规则。引用的规则变量均来自基础数据库架构。然后,将生成规则转换为中间面向查询的 DSL(例如,LINQ)。最后,通过 ORM(对象关系映射)自动将中间 DSL 直接映射到原生数据库查询(例如,SQL)。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1683/5396472/e1913269905b/JHE2017-7862672.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1683/5396472/1db998df879c/JHE2017-7862672.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1683/5396472/8d84ac7eafde/JHE2017-7862672.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1683/5396472/e1913269905b/JHE2017-7862672.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1683/5396472/1db998df879c/JHE2017-7862672.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1683/5396472/8d84ac7eafde/JHE2017-7862672.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1683/5396472/e1913269905b/JHE2017-7862672.003.jpg

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