Payne Philip R O, Borlawsky Tara B, Kwok Alan, Greaves Andrew W
The Ohio State University, Department of Biomedical Informatics, Columbus, OH, USA.
AMIA Annu Symp Proc. 2008 Nov 6;2008:566-70.
The ability to generate hypotheses based upon the contents of large-scale, heterogeneous data sets is critical to the design of translational clinical studies. In previous reports, we have described the application of a conceptual knowledge engineering technique, known as constructive induction (CI) in order to satisfy such needs. However, one of the major limitations of this method is the need to engage multiple subject matter experts to verify potential hypotheses generated using CI. In this manuscript, we describe an alternative verification technique that leverages published biomedical literature abstracts. Our report will be framed in the context of an ongoing project to generate hypotheses related to the contents of a translational research data repository maintained by the CLL Research Consortium. Such hypotheses will are intended to inform the design of prospective clinical studies that can elucidate the relationships that may exist between biomarkers and patient phenotypes.
基于大规模、异构数据集的内容生成假设的能力对于转化临床研究的设计至关重要。在之前的报告中，我们描述了一种概念性知识工程技术——构造性归纳法（CI）的应用，以满足此类需求。然而，该方法的主要局限性之一是需要多名主题专家来验证使用CI生成的潜在假设。在本手稿中，我们描述了一种利用已发表的生物医学文献摘要的替代验证技术。我们的报告将围绕一个正在进行的项目展开，该项目旨在生成与慢性淋巴细胞白血病研究联盟维护的转化研究数据存储库内容相关的假设。此类假设旨在为前瞻性临床研究的设计提供信息，从而阐明生物标志物与患者表型之间可能存在的关系。
