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A sequence labeling approach to link medications and their attributes in clinical notes and clinical trial announcements for information extraction J Am Med Inform Assoc-2013-Li-915-21
基于序列标引的药物以及其属性的抽取方法
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 multilayered
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 rulebased
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.
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