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. 2013 Sep-Oct;20(5):915-21.
doi: 10.1136/amiajnl-2012-001487. Epub 2012 Dec 25.

A sequence labeling approach to link medications and their attributes in clinical notes and clinical trial announcements for information extraction

Qi Li  1 , Haijun Zhai, Louise Deleger, Todd Lingren, Megan Kaiser, Laura Stoutenborough, Imre Solti
Affiliations

A sequence labeling approach to link medications and their attributes in clinical notes and clinical trial announcements for information extraction

Qi Li et al. J Am Med Inform Assoc. 2013 Sep-Oct.

Abstract

Objective: The goal of this work was to evaluate machine learning methods, binary classification and sequence labeling, for medication-attribute linkage detection in two clinical corpora.

Data and methods: We double annotated 3000 clinical trial announcements (CTA) and 1655 clinical notes (CN) for medication named entities and their attributes. A binary support vector machine (SVM) classification method with parsimonious feature sets, and a conditional random fields (CRF)-based multi-layered sequence labeling (MLSL) model were proposed to identify the linkages between the entities and their corresponding attributes. We evaluated the system's performance against the human-generated gold standard.

Results: The experiments showed that the two machine learning approaches performed statistically significantly better than the baseline rule-based approach. The binary SVM classification achieved 0.94 F-measure with individual tokens as features. The SVM model trained on a parsimonious feature set achieved 0.81 F-measure for CN and 0.87 for CTA. The CRF MLSL method achieved 0.80 F-measure on both corpora.

Discussion and conclusions: We compared the novel MLSL method with a binary classification and a rule-based method. The MLSL method performed statistically significantly better than the rule-based method. However, the SVM-based binary classification method was statistically significantly better than the MLSL method for both the CTA and CN corpora. Using parsimonious feature sets both the SVM-based binary classification and CRF-based MLSL methods achieved high performance in detecting medication name and attribute linkages in CTA and CN.

Keywords: attribute linkages; clinical notes; clinical trial announcements; multi-layered sequence labeling; natural language processing.

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Figures

Figure 1
Figure 1
Examples of linkages between medications and their attributes. Access the article online to view this figure in colour.
Figure 2
Figure 2
Features for binary classification-based linkage detection.
Figure 3
Figure 3
Examples of representative medication–attribute linkages using the multi-layered sequence labeling model. Access the article online to view this figure in colour.
Figure 4
Figure 4
Features for the multi-layered sequence labeling.
Figure 5
Figure 5
Evaluation measures.
See this image and copyright information in PMC

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