research-article

Clinical entity recognition using structural support vector machines with rich features

Authors:

Hua XuAuthors Info & Claims

DTMBIO '12: Proceedings of the ACM sixth international workshop on Data and text mining in biomedical informatics

Pages 13 - 20

https://doi.org/10.1145/2390068.2390073

Published: 29 October 2012 Publication History

Abstract

Named entity recognition (NER) is an important task for natural language processing (NLP) of clinical text. Conditional Random Fields (CRFs), a sequential labeling algorithm, and Support Vector Machines (SVMs), which is based on large margin theory, are two typical machine learning algorithms that have been widely applied to NER tasks, including clinical entity recognition. However, Structural Support Vector Machines (SSVMs), an algorithm that combines the advantages of both CRFs and SVMs, has not been investigated for clinical text processing. In this study, we applied the SSVMs algorithm to the Concept Extraction task of the 2010 i2b2 clinical NLP challenge, which was to recognize entities of medical problems, treatments, and tests from hospital discharge summaries. Using the same training (N = 27,837) and test (N = 45,009) sets in the challenge, our evaluation showed that the SSVMs-based NER system required less training time, while achieved better performance than the CRFs-based system for clinical entity recognition, when same features were used. Our study also demonstrated that rich features such as unsupervised word representations improved the performance of clinical entity recognition. When rich features were integrated with SSVMs, our system achieved a highest F-measure of 85.74% on the test set of 2010 i2b2 NLP challenge, which outperformed the best system reported in the challenge by 0.5%.

References

[1]

Friedman, C., Alderson, P. O., Austin, J. H., Cimino, J. J. and Johnson, S. B. A general natural-language text processor for clinical radiology. J Am Med Inform Assoc, 1, 2 (Mar-Apr 1994), 161--174.

[2]

Meystre, S. M., Savova, G. K., Kipper-Schuler, K. C. and Hurdle, J. F. Extracting information from textual documents in the electronic health record: a review of recent research. Yearb Med Inform 2008, 128--144.

[3]

Haug, P. J., Koehler, S., Lau, L. M., Wang, P., Rocha, R. and Huff, S. M. Experience with a mixed semantic/syntactic parser. Proc Annu Symp Comput Appl Med Care 1995, 284--288.

[4]

Haug, P. J., Christensen, L., Gundersen, M., Clemons, B., Koehler, S. and Bauer, K. A natural language parsing system for encoding admitting diagnoses. Proc AMIA Annu Fall Symp 1997, 814--818.

[5]

Aronson, A. R. and Lang, F. M. An overview of MetaMap: historical perspective and recent advances. J Am Med Inform Assoc, 17, 3 (May 1 2010), 229--236.

[6]

Denny, J. C., Miller, R. A., Johnson, K. B. and Spickard, A., 3rd Development and evaluation of a clinical note section header terminology. AMIA Annu Symp Proc 2008, 156--160.

[7]

Savova, G. K., Masanz, J. J., Ogren, P. V., Zheng, J., Sohn, S., Kipper-Schuler, K. C. and Chute, C. G. Mayo clinical Text Analysis and Knowledge Extraction System (cTAKES): architecture, component evaluation and applications. J Am Med Inform Assoc, 17, 5 (Sep-Oct 2010), 507--513.

[8]

Zeng, Q. T., Goryachev, S., Weiss, S., Sordo, M., Murphy, S. N. and Lazarus, R. Extracting principal diagnosis, co-morbidity and smoking status for asthma research: evaluation of a natural language processing system. BMC Med Inform Decis Mak, 62006, 30.

[9]

Uzuner, O., Solti, I. and Cadag, E. Extracting medication information from clinical text. J Am Med Inform Assoc, 17, 5 (Sep-Oct 2010), 514--518.

[10]

Uzuner, O., South, B. R., Shen, S. and DuVall, S. L. 2010 i2b2/VA challenge on concepts, assertions, and relations in clinical text. J Am Med Inform Assoc, 18, 5 (Sep-Oct 2011), 552--556.

[11]

Doan, S., Bastarache, L., Klimkowski, S., Denny, J. C. and Xu, H. Integrating existing natural language processing tools for medication extraction from discharge summaries. J Am Med Inform Assoc, 17, 5 (Sep-Oct 2010), 528--531.

[12]

Spasic, I., Sarafraz, F., Keane, J. A. and Nenadic, G. Medication information extraction with linguistic pattern matching and semantic rules. J Am Med Inform Assoc, 17, 5 (Sep-Oct 2010), 532--535.

[13]

Patrick, J. and Li, M. High accuracy information extraction of medication information from clinical notes: 2009 i2b2 medication extraction challenge. J Am Med Inform Assoc, 17, 5 (Sep-Oct 2010), 524--527.

[14]

Li, Z., Liu, F., Antieau, L., Cao, Y. and Yu, H. Lancet: a high precision medication event extraction system for clinical text. J Am Med Inform Assoc, 17, 5 (Sep-Oct 2010), 563--567.

[15]

Meystre, S. M., Thibault, J., Shen, S., Hurdle, J. F. and South, B. R. Textractor: a hybrid system for medications and reason for their prescription extraction from clinical text documents. J Am Med Inform Assoc, 17, 5 (Sep-Oct 2010), 559--562.

[16]

de Bruijn, B., Cherry, C., Kiritchenko, S., Martin, J. and Zhu, X. Machine-learned solutions for three stages of clinical information extraction: the state of the art at i2b2 2010. J Am Med Inform Assoc, 18, 5 (Sep-Oct 2011), 557--562.

[17]

Jiang, M., Chen, Y., Liu, M., Rosenbloom, S. T., Mani, S., Denny, J. C. and Xu, H. A study of machine-learning-based approaches to extract clinical entities and their assertions from discharge summaries. J Am Med Inform Assoc, 18, 5 (Sep-Oct 2011), 601--606.

[18]

Torii, M., Wagholikar, K. and Liu, H. Using machine learning for concept extraction on clinical documents from multiple data sources. J Am Med Inform Assoc, 18, 5 (Sep-Oct 2011), 580--587.

[19]

Li, D., Kipper-Schuler, K. and Savova, G. Conditional random fields and support vector machines for disorder named entity recognition in clinical texts. In Proceedings of the Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing (Columbus, Ohio, 2008). Association for Computational Linguistics.

Digital Library

[20]

Wu, Y.-C., Fan, T.-K., Lee, Y.-S. and Yen, S.-J. Extracting named entities using support vector machines. In Proceedings of the Proceedings of the 2006 international conference on Knowledge Discovery in Life Science Literature (Singapore, 2006). Springer-Verlag.

Digital Library

[21]

Kudoh, T. and Matsumoto, Y. Use of support vector learning for chunk identification. In Proceedings of the Proceedings of the 2nd workshop on Learning language in logic and the 4th conference on Computational natural language learning - Volume 7 (Lisbon, Portugal, 2000). Association for Computational Linguistics.

Digital Library

[22]

Kudo, T. and Matsumoto, Y. Chunking with support vector machines. In Proceedings of the Proceedings of the second meeting of the North American Chapter of the Association for Computational Linguistics on Language technologies (Pittsburgh, Pennsylvania, 2001). Association for Computational Linguistics.

Digital Library

[23]

Tsochantaridis, I., Joachims, T., Hofmann, T. and Altun, Y. Large Margin Methods for Structured and Interdependent Output Variables. J. Mach. Learn. Res., 62005), 1453--1484.

Digital Library

[24]

Joachims, T., Finley, T. and Yu, C.-N. J. Cutting-plane training of structural SVMs. Mach. Learn., 77, 1 2009, 27--59.

Digital Library

[25]

He, Y. and Kayaalp, M. Biological entity recognition with conditional random fields. AMIA Annu Symp Proc 2008, 293--297.

[26]

Song, Y., Kim, E., Lee, G. G. and Yi, B. K. POSBIOTM-NER: a trainable biomedical named-entity recognition system. Bioinformatics, 21, 11 (Jun 1 2005), 2794--2796.

Digital Library

[27]

Miller, S. a. G., Jethran and Zamanian, Alex. Name Tagging with Word Clusters and Discriminative Training. In Proceedings of the HLT-NAACL (2004).

[28]

Turian, J., Ratinov, L. and Bengio, Y. Word representations: a simple and general method for semi-supervised learning. In Proceedings of the Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics (Uppsala, Sweden, 2010). Association for Computational Linguistics.

Digital Library

[29]

Brown, P. F., deSouza, P. V., Mercer, R. L., Pietra, V. J. D. and Lai, J. C. Class-based n-gram models of natural language. Comput. Linguist., 18, 4 1992), 467--479.

Digital Library

[30]

Sang, E. F. T. K. and Veenstra, J. Representing text chunks. In Proceedings of the Ninth Conference of the European Chapter of the Association for Computational Linguistics (1999).

Digital Library

[31]

Wilcoxon, F. Individual Comparisons by Ranking Methods. Biometrics Bulletin, 1, 6 1945, 80--83.

[32]

Kang, N., Afzal, Z., Singh, B., van Mulligen, E. M. and Kors, J. A. Using an ensemble system to improve concept extraction from clinical records. Journal of Biomedical Informatics, 45, 3 2012, 423--428.

Digital Library

[33]

Gurulingappa, H., H.-A. M., Fluck, J. Concept identification and assertion classification in patient health records. In Proceedings of the Proceedings of the 2010 i2b2/VA Workshop on Challenges in Natural Language Processing for Clinical Data (Boston, MA, USA, 2010).

[34]

Patrick, J. D., Nguyen, D. H. M., Wang, Y. and Li, M. A knowledge discovery and reuse pipeline for information extraction in clinical notes. Journal of the American Medical Informatics Association, 18, 5 (September 1, 2011 2011), 574--579.

[35]

Nguyen, N. and Guo, Y. Comparisons of sequence labeling algorithms and extensions. In Proceedings of the Proceedings of the 24th international conference on Machine learning (Corvalis, Oregon, 2007). ACM.

Digital Library

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Mo DHuang BWang HCao XGan KWei JWeng HHao T(2024)SCLert: A Span-Based Joint Model for Measurable Quantitative Information Extraction From Chinese TextsIEEE Transactions on Consumer Electronics10.1109/TCE.2023.332768170:1(3361-3371)Online publication date: Feb-2024
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Alamro HGojobori TEssack MGao X(2024)BioBBC: a multi-feature model that enhances the detection of biomedical entitiesScientific Reports10.1038/s41598-024-58334-x14:1Online publication date: 2-Apr-2024
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Index Terms

Clinical entity recognition using structural support vector machines with rich features
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Language resources

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Published In

cover image ACM Conferences

DTMBIO '12: Proceedings of the ACM sixth international workshop on Data and text mining in biomedical informatics

October 2012

92 pages

ISBN:9781450317160

DOI:10.1145/2390068

General Chairs:
Doheon Kim
KAIST, Korea
,
Sophia Ananiadou
University of Manchester, UK
,
Program Chairs:
Min Song
Yonsei University, USA
,
Hua Xu
Vanderbilt University, USA

Copyright © 2012 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 29 October 2012

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October 29, 2012

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https://doi.org/10.1109/TCE.2023.3327681
Alamro HGojobori TEssack MGao X(2024)BioBBC: a multi-feature model that enhances the detection of biomedical entitiesScientific Reports10.1038/s41598-024-58334-x14:1Online publication date: 2-Apr-2024
https://doi.org/10.1038/s41598-024-58334-x
Peng CYang XSmith KYu ZChen ABian JWu Y(2024)Model tuning or prompt Tuning? a study of large language models for clinical concept and relation extractionJournal of Biomedical Informatics10.1016/j.jbi.2024.104630153(104630)Online publication date: May-2024
https://doi.org/10.1016/j.jbi.2024.104630
Zhang QChen JWang HCao XJie WHao T(2024)ESert: An Enhanced Span-Based Model for Measurable Quantitative Information Extraction from Medical TextsNeural Computing for Advanced Applications10.1007/978-981-97-7007-6_2(17-32)Online publication date: 22-Sep-2024
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Peng CYang XYu ZBian JHogan WWu Y(2023)Clinical concept and relation extraction using prompt-based machine reading comprehensionJournal of the American Medical Informatics Association10.1093/jamia/ocad10730:9(1486-1493)Online publication date: 14-Jun-2023
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Li JWei QGhiasvand OChen MLobanov VWeng CXu H(2022)A comparative study of pre-trained language models for named entity recognition in clinical trial eligibility criteria from multiple corporaBMC Medical Informatics and Decision Making10.1186/s12911-022-01967-722:S3Online publication date: 6-Sep-2022
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