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

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. PMID: 23268488 [PubMed - as supplied by publisher]
Source: Journal of the American Medical Informatics Association - Category: Information Technology Authors: Tags: J Am Med Inform Assoc Source Type: research

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