Linking Medications and Their Attributes in Clinical Notes and Clinical Trial Announcements for Information Extraction: A Sequence Labeling Approach

Author

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

Author_Institution

Med. Center, Div. of Biomed. Inf., Cincinnati Children´´s Hosp., Cincinnati, OH, USA

fYear

2012

fDate

27-28 Sept. 2012

Firstpage

84

Lastpage

84

Abstract

The goal of this work is to evaluate binary classification and sequence labeling methods for medication-attribute linkage detection in two clinical corpora. The results show that with parsimonious feature sets both the Support Vector Machine (SVM)-based binary classification and Conditional Random Field (CRF)-based multi-layered sequence labeling methods are achieving high performance.

Keywords

information retrieval; medical administrative data processing; medicine; pattern classification; statistical analysis; support vector machines; CRF-based multilayered sequence labeling; SVM-based binary classification; clinical notes; clinical trial announcements; conditional random field-based multilayered sequence labeling; information extraction; medication-attribute linkage detection; parsimonious feature sets; support vector machine-based binary classification; Biological system modeling; Biomedical imaging; Couplings; Joining processes; Labeling; Medical services; Support vector machines;

fLanguage

English

Publisher

ieee

Conference_Titel

Healthcare Informatics, Imaging and Systems Biology (HISB), 2012 IEEE Second International Conference on

Conference_Location

San Diego, CA

Print_ISBN

978-1-4673-4803-4

Type

conf

DOI

10.1109/HISB.2012.27

Filename

6366192