Garcia-Gathright Jean I, Matiasz Nicholas J, Garon Edward B, Aberle Denise R, Taira Ricky K, Bui Alex A T
University of California Los Angeles, Department of Bioengineering.
University of California Los Angeles, Department of Medicine.
IEEE EMBS Int Conf Biomed Health Inform. 2016 Feb;2016:449-452. doi: 10.1109/BHI.2016.7455931. Epub 2016 Apr 21.
As the volume of biomedical literature increases, it can be challenging for clinicians to stay up-to-date. Graphical summarization systems help by condensing knowledge into networks of entities and relations. However, existing systems present relations out of context, ignoring key details such as study population. To better support precision medicine, summarization systems should include such information to contextualize and tailor results to individual patients. This paper introduces "contextualized semantic maps" for patient-tailored graphical summarization of published literature. These efforts are demonstrated in the domain of driver mutations in non-small cell lung cancer (NSCLC). A representation for relations and study population context in NSCLC was developed. An annotated gold standard for this representation was created from a set of 135 abstracts; F1-score annotator agreement was 0.78 for context and 0.68 for relations. Visualizing the contextualized relations demonstrated that context facilitates the discovery of key findings that are relevant to patient-oriented queries.
随着生物医学文献数量的增加，临床医生要跟上最新进展可能具有挑战性。图形化总结系统通过将知识浓缩为实体和关系网络来提供帮助。然而，现有系统呈现的关系脱离了上下文，忽略了诸如研究人群等关键细节。为了更好地支持精准医学，总结系统应纳入此类信息，以便根据个体患者的情况对结果进行背景化处理和定制。本文介绍了用于已发表文献的患者定制图形化总结的“上下文语义图”。这些成果在非小细胞肺癌（NSCLC）驱动基因突变领域得到了展示。开发了一种NSCLC中关系和研究人群背景的表示方法。从一组135篇摘要中创建了该表示方法的注释金标准；上下文的F1分数注释者一致性为0.78，关系的F1分数注释者一致性为0.68。可视化上下文关系表明，上下文有助于发现与面向患者的查询相关的关键发现。
