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A sequence labeling approach to link medications and their attributes in clinical notes and clinical trial announcements for information extraction
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 m...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BMJ Publishing Group
2013
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3756265/ https://www.ncbi.nlm.nih.gov/pubmed/23268488 http://dx.doi.org/10.1136/amiajnl-2012-001487 |
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author | Li, Qi Zhai, Haijun Deleger, Louise Lingren, Todd Kaiser, Megan Stoutenborough, Laura Solti, Imre |
author_facet | Li, Qi Zhai, Haijun Deleger, Louise Lingren, Todd Kaiser, Megan Stoutenborough, Laura Solti, Imre |
author_sort | Li, Qi |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-3756265 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | BMJ Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-37562652013-12-11 A sequence labeling approach to link medications and their attributes in clinical notes and clinical trial announcements for information extraction Li, Qi Zhai, Haijun Deleger, Louise Lingren, Todd Kaiser, Megan Stoutenborough, Laura Solti, Imre J Am Med Inform Assoc Research and Applications 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. BMJ Publishing Group 2013-09 2012-12-25 /pmc/articles/PMC3756265/ /pubmed/23268488 http://dx.doi.org/10.1136/amiajnl-2012-001487 Text en Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 3.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/3.0/ |
spellingShingle | Research and Applications Li, Qi Zhai, Haijun Deleger, Louise Lingren, Todd Kaiser, Megan Stoutenborough, Laura Solti, Imre A sequence labeling approach to link medications and their attributes in clinical notes and clinical trial announcements for information extraction |
title | A sequence labeling approach to link medications and their attributes in clinical notes and clinical trial announcements for information extraction |
title_full | A sequence labeling approach to link medications and their attributes in clinical notes and clinical trial announcements for information extraction |
title_fullStr | A sequence labeling approach to link medications and their attributes in clinical notes and clinical trial announcements for information extraction |
title_full_unstemmed | A sequence labeling approach to link medications and their attributes in clinical notes and clinical trial announcements for information extraction |
title_short | A sequence labeling approach to link medications and their attributes in clinical notes and clinical trial announcements for information extraction |
title_sort | sequence labeling approach to link medications and their attributes in clinical notes and clinical trial announcements for information extraction |
topic | Research and Applications |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3756265/ https://www.ncbi.nlm.nih.gov/pubmed/23268488 http://dx.doi.org/10.1136/amiajnl-2012-001487 |
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