Sun Weiyi, Rumshisky Anna, Uzuner Ozlem
Department of Informatics, University at Albany, SUNY. Albany, NY
Department of Computer Science, University of Massachusetts Lowell. Lowell, MA.
J Am Med Inform Assoc. 2015 Sep;22(5):1001-8. doi: 10.1093/jamia/ocu004. Epub 2015 Apr 12.
To improve the normalization of relative and incomplete temporal expressions (RI-TIMEXes) in clinical narratives.
We analyzed the RI-TIMEXes in temporally annotated corpora and propose two hypotheses regarding the normalization of RI-TIMEXes in the clinical narrative domain: the anchor point hypothesis and the anchor relation hypothesis. We annotated the RI-TIMEXes in three corpora to study the characteristics of RI-TMEXes in different domains. This informed the design of our RI-TIMEX normalization system for the clinical domain, which consists of an anchor point classifier, an anchor relation classifier, and a rule-based RI-TIMEX text span parser. We experimented with different feature sets and performed an error analysis for each system component.
The annotation confirmed the hypotheses that we can simplify the RI-TIMEXes normalization task using two multi-label classifiers. Our system achieves anchor point classification, anchor relation classification, and rule-based parsing accuracy of 74.68%, 87.71%, and 57.2% (82.09% under relaxed matching criteria), respectively, on the held-out test set of the 2012 i2b2 temporal relation challenge.
Experiments with feature sets reveal some interesting findings, such as: the verbal tense feature does not inform the anchor relation classification in clinical narratives as much as the tokens near the RI-TIMEX. Error analysis showed that underrepresented anchor point and anchor relation classes are difficult to detect.
We formulate the RI-TIMEX normalization problem as a pair of multi-label classification problems. Considering only RI-TIMEX extraction and normalization, the system achieves statistically significant improvement over the RI-TIMEX results of the best systems in the 2012 i2b2 challenge.
提高临床叙述中相对和不完整时间表达（RI-TIMEXes）的规范化程度。
我们分析了带有时间标注的语料库中的RI-TIMEXes，并提出了关于临床叙述领域中RI-TIMEXes规范化的两个假设：锚点假设和锚点关系假设。我们在三个语料库中对RI-TIMEXes进行了标注，以研究不同领域中RI-TIMEXes的特征。这为我们针对临床领域的RI-TIMEX规范化系统的设计提供了依据，该系统由一个锚点分类器、一个锚点关系分类器和一个基于规则的RI-TIMEX文本跨度解析器组成。我们对不同的特征集进行了实验，并对每个系统组件进行了错误分析。
标注证实了我们可以使用两个多标签分类器简化RI-TIMEXes规范化任务的假设。在2012年i2b2时间关系挑战赛的留出测试集上，我们的系统分别实现了74.68%、87.71%和57.2%（在宽松匹配标准下为82.09%）的锚点分类、锚点关系分类和基于规则的解析准确率。
对特征集的实验揭示了一些有趣的发现，例如：动词时态特征在临床叙述中对锚点关系分类的作用不如RI-TIMEX附近的词元。错误分析表明，代表性不足的锚点和锚点关系类别难以检测。
我们将RI-TIMEX规范化问题表述为一对多标签分类问题。仅考虑RI-TIMEX提取和规范化，该系统相对于2012年i2b2挑战赛中最佳系统的RI-TIMEX结果有统计学上的显著改进。
