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基于本体的时间推理与分析的晚期支架血栓形成用例研究。

A use case study on late stent thrombosis for ontology-based temporal reasoning and analysis.

作者信息

Clark Kim, Sharma Deepak, Qin Rui, Chute Christopher G, Tao Cui

机构信息

Boston Scientific Corporation, Maple Grove, MN USA.

Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN USA.

出版信息

J Biomed Semantics. 2014 Dec 11;5(1):49. doi: 10.1186/2041-1480-5-49. eCollection 2014.

Abstract

In this paper, we show how we have applied the Clinical Narrative Temporal Relation Ontology (CNTRO) and its associated temporal reasoning system (the CNTRO Timeline Library) to trend temporal information within medical device adverse event report narratives. 238 narratives documenting occurrences of late stent thrombosis adverse events from the Food and Drug Administration's (FDA) Manufacturing and User Facility Device Experience (MAUDE) database were annotated and evaluated using the CNTRO Timeline Library to identify, order, and calculate the duration of temporal events. The CNTRO Timeline Library had a 95% accuracy in correctly ordering events within the 238 narratives. 41 narratives included an event in which the duration was documented, and the CNTRO Timeline Library had an 80% accuracy in correctly determining these durations. 77 narratives included documentation of a duration between events, and the CNTRO Timeline Library had a 76% accuracy in determining these durations. This paper also includes an example of how this temporal output from the CNTRO ontology can be used to verify recommendations for length of drug administration, and proposes that these same tools could be applied to other medical device adverse event narratives in order to identify currently unknown temporal trends.

摘要

在本文中,我们展示了如何应用临床叙事时间关系本体(CNTRO)及其相关的时间推理系统(CNTRO时间线库)来梳理医疗设备不良事件报告叙事中的时间信息。使用CNTRO时间线库对来自美国食品药品监督管理局(FDA)的制造与用户设施设备经验(MAUDE)数据库的238份记录晚期支架血栓形成不良事件发生情况的叙事进行注释和评估,以识别、排序并计算时间事件的持续时间。CNTRO时间线库在正确排序这238份叙事中的事件方面准确率达95%。41份叙事包含记录了持续时间的事件,CNTRO时间线库在正确确定这些持续时间方面准确率达80%。77份叙事包含事件之间持续时间的记录,CNTRO时间线库在确定这些持续时间方面准确率达76%。本文还给出了一个示例,说明如何利用CNTRO本体的这个时间输出结果来验证给药时长的建议,并提出这些相同的工具可应用于其他医疗设备不良事件叙事,以识别当前未知的时间趋势。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/04ed/4275934/8182b2bdfb2a/13326_2014_191_Fig1_HTML.jpg

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