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临床研究信息学:2016年的贡献

Clinical Research Informatics: Contributions from 2016.

作者信息

Daniel C, Choquet R

出版信息

Yearb Med Inform. 2017 Aug;26(1):209-213. doi: 10.15265/IY-2017-024. Epub 2017 Sep 11.

Abstract

To summarize key contributions to current research in the field of Clinical Research Informatics (CRI) and to select the best papers published in 2016. A bibliographic search using a combination of MeSH and free terms on CRI was performed using PubMed, followed by a double-blind review in order to select a list of candidate best papers to be then peer-reviewed by external reviewers. A consensus meeting between the two section editors and the editorial team was organized to finally conclude on the selection of best papers. Among the 452 papers published in 2016 in the various areas of CRI and returned by the query, the full review process selected four best papers. The authors of the first paper utilized a comprehensive representation of the patient medical record and semi-automatically labeled training sets to create phenotype models via a machine learning process. The second selected paper describes an open source tool chain securely connecting ResearchKit compatible applications (Apps) to the widely-used clinical research infrastructure Informatics for Integrating Biology and the Bedside (i2b2). The third selected paper describes the FAIR Guiding Principles for scientific data management and stewardship. The fourth selected paper focuses on the evaluation of the risk of privacy breaches in releasing genomics datasets. A major trend in the 2016 publications is the variety of research on "real-world data" - healthcare-generated data, person health data, and patient-reported outcomes -highlighting the opportunities provided by new machine learning techniques as well as new potential risks of privacy breaches.

摘要

总结对临床研究信息学(CRI)领域当前研究的关键贡献,并选出2016年发表的最佳论文。使用PubMed通过结合医学主题词(MeSH)和关于CRI的自由词进行文献检索,随后进行双盲评审,以选出候选最佳论文列表,然后由外部评审员进行同行评审。组织了两位栏目编辑和编辑团队之间的共识会议,最终确定最佳论文的选择。在2016年发表在CRI各个领域且由查询返回的452篇论文中,完整的评审过程选出了四篇最佳论文。第一篇论文的作者利用患者病历的全面表示和半自动标记的训练集,通过机器学习过程创建表型模型。第二篇入选论文描述了一个开源工具链,它将与ResearchKit兼容的应用程序(Apps)安全地连接到广泛使用的临床研究基础设施“整合生物学与床边信息学”(i2b2)。第三篇入选论文描述了科学数据管理和监管的FAIR指导原则。第四篇入选论文专注于评估发布基因组数据集时隐私泄露的风险。2016年出版物中的一个主要趋势是对“真实世界数据”的各种研究——医疗保健产生的数据、个人健康数据和患者报告的结果——突出了新机器学习技术带来的机遇以及隐私泄露的新潜在风险。

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本文引用的文献

8
Electronic medical record phenotyping using the anchor and learn framework.使用锚定与学习框架进行电子病历表型分析。
J Am Med Inform Assoc. 2016 Jul;23(4):731-40. doi: 10.1093/jamia/ocw011. Epub 2016 Apr 23.

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