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

Li Q, Zhai H, Deleger L, Lingren T, Kaiser M, Stoutenborough L, Solti I - J Am Med Inform Assoc (2012)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA.

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.

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Evaluation measures.
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AMIAJNL2012001487F5: Evaluation measures.

Mentions: In order to evaluate the linkage detection methods, we conducted the experiments on the task of identifying the linkages between the medication entities and nine attributes. We evaluated two machine learning approaches for linkage detection, a binary classification-based method and a MLSL method. Three standard NLP metrics to measure performance: precision (positive predictive value), recall (sensitivity), and F-measure (F), were used for the evaluation. They were defined as shown in figure 5.


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

Li Q, Zhai H, Deleger L, Lingren T, Kaiser M, Stoutenborough L, Solti I - J Am Med Inform Assoc (2012)

Evaluation measures.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3756265&req=5

AMIAJNL2012001487F5: Evaluation measures.
Mentions: In order to evaluate the linkage detection methods, we conducted the experiments on the task of identifying the linkages between the medication entities and nine attributes. We evaluated two machine learning approaches for linkage detection, a binary classification-based method and a MLSL method. Three standard NLP metrics to measure performance: precision (positive predictive value), recall (sensitivity), and F-measure (F), were used for the evaluation. They were defined as shown in figure 5.

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA.

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.

Show MeSH