Normalization of Relative and Incomplete Temporal Expressions in Clinical Narratives

TL;DR

This paper presents a system for normalizing relative and incomplete temporal expressions in clinical narratives, using multi-label classifiers for anchor points and relations, achieving significant accuracy improvements over previous methods.

Contribution

The study introduces a novel approach to RI-TIMEX normalization in clinical texts using multi-label classifiers, simplifying the task and improving accuracy over prior systems.

Findings

Achieved 74.68% anchor point classification accuracy.

Achieved 87.71% anchor relation classification accuracy.

System outperforms previous best systems in the 2012 i2b2 challenge.

Abstract

We analyze 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 annotate 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 experiment with different feature sets and perform 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 reveals 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 shows 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 the 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.