Department of Biomedical Informatics, Vanderbilt University School of Medicine , Nashville, Tennessee, USA.
Department of Biostatistics, Vanderbilt University School of Medicine, Nashville, Tennessee, USA; 4School of Nursing, Vanderbilt University , Nashville, Tennessee, USA.
Appl Clin Inform. 2013 Dec 18;4(4):596-617. doi: 10.4338/ACI-2013-08-RA-0062. eCollection 2013.
This work identified challenges associated with extraction and representation of medication-related information from publicly available electronic sources.
We gained direct observational experience through creating and evaluating the Drug Evidence Base (DEB), a repository of drug indications and adverse effects (ADEs), and supplemented this through literature review. We extracted DEB content from the National Drug File Reference Terminology, from aggregated MEDLINE co-occurrence data, and from the National Library of Medicine's DailyMed. To understand better the similarities, differences and problems with the content of DEB and the SIDER Side Effect Resource, and Vanderbilt's MEDI Indication Resource, we carried out statistical evaluations and human expert reviews.
While DEB, SIDER, and MEDI often agreed on medication indications and side effects, cross-system shortcomings limit their current utility. The drug information resources we evaluated frequently employed multiple, disparate vaguely related UMLS concepts to represent a single specific clinical drug indication or adverse effect. Thus, evaluations comparing drug-indication and drug-ADE coverage for such resources will encounter substantial numbers of false negative and false positive matches. Furthermore, our review found that many indication and ADE relationships are too complex - logically and temporally - to represent within existing systems.
To enhance applicability and utility, future drug information systems deriving indications and ADEs from public resources must represent clinical concepts uniformly and as precisely as possible. Future systems must also better represent the inherent complexity of indications and ADEs.
本研究旨在确定从公开可用的电子资源中提取和表示药物相关信息所面临的挑战。
我们通过创建和评估药物证据库（Drug Evidence Base，DEB）获得了直接的观察经验，该库是药物适应证和不良反应（Adverse Effect，AE）的存储库，并通过文献回顾对此进行了补充。我们从国家药物文件参考术语（National Drug File Reference Terminology）、汇总的 MEDLINE 共现数据以及美国国家医学图书馆的每日医学（DailyMed）中提取 DEB 内容。为了更好地理解 DEB 与 SIDER 副作用资源以及范德比尔特大学的 MEDI 适应证资源的内容的相似之处、差异和问题，我们进行了统计评估和人工专家审查。
尽管 DEB、SIDER 和 MEDI 通常在药物适应证和副作用方面达成一致，但跨系统的局限性限制了它们的当前效用。我们评估的药物信息资源经常使用多个不同的、模糊相关的 UMLS 概念来表示单个特定的临床药物适应证或不良反应。因此，对于这些资源，评估药物适应证和药物 ADE 覆盖范围的比较将会遇到大量的假阴性和假阳性匹配。此外，我们的审查发现，许多适应证和 ADE 关系在逻辑和时间上过于复杂，无法在现有系统中表示。
为了提高适用性和实用性，未来从公共资源中提取适应证和 ADE 的药物信息系统必须尽可能统一和精确地表示临床概念。未来的系统还必须更好地表示适应证和 ADE 的固有复杂性。
