research-article

REMed: automatic relation extraction from medical documents

Authors:

Mihaela Porumb,

Ioana Barbantan,

Camelia Lemnaru,

Rodica PotoleaAuthors Info & Claims

iiWAS '15: Proceedings of the 17th International Conference on Information Integration and Web-based Applications & Services

Article No.: 19, Pages 1 - 10

https://doi.org/10.1145/2837185.2837239

Published: 11 December 2015 Publication History

Abstract

The large amount of unstructured medical documents written in natural language bears a massive quantity of knowledge, whose extraction becomes useful. An automatic relation identification strategy leads to the discovery of relations, (possible unknown) interactions, and associations between medical conditions, investigations and treatments. The current paper introduces a learning based approach for the automatic discovery of relations between medical concepts, entitled REMed. We propose an original list of features, grouped into four categories with the following distribution: lexical - 3, context - 6, grammatical -- 4 and syntactic - 4. We analyzed the influence of each category on the classification performance and determined that the performance of the REMed solution is comparable with similar solutions. We report the overall F-measure as 74.9% that outperforms the best solution reported in the similar systems with 1.2%. This performance was achieved mostly by the features from the lexical and context categories.

References

[1]

Albin A, Ji X, Borlawsky TB, Ye Z, Lin S, Payne PR, Huang K, Xiang Y. Enabling Online Studies of Conceptual Relationships Between Medical Terms: Developing an Efficient Web Platform. JMIR Med Inform. 2014; 2(2):e23. URL: http://medinform.jmir.org/2014/2/e23.

[2]

Anick P, Hong P, Xue N, et al. 2010. I2B2 2010 challenge: machine learning for information extraction from patient records. In Proceedings of the 2010 i2b2/VA Workshop on Challenges in Natural Language Processing for Clinical Data. Boston, MA, USA: i2b2.

[3]

Aronson AR, Lang FM. 2010. An overview of MetaMap: historical perspective and recent advances. J Am Med Inform Assoc 2010; 17:229--36.

[4]

de Bruijn B, Cherry C, Kiritchenko S, et al. 2010. NRC at i2b2: one challenge, three practical tasks, nine statistical systems, hundreds of clinical records, millions of useful features. In Proceedings of the 2010 i2b2/VA Workshop on Challenges in Natural Language Processing for Clinical Data. Boston, MA, USA: i2b2.

[5]

R. Bunescu and R. Mooney 2005. A shortest path dependency kernel for relation extraction. In Proceedings of the Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing (HLT/EMNLP-05)

Digital Library

[6]

Chang C. C., Lin C. J. 2011. LIBSVM: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology.

[7]

Collins M., Mitchell P. Marcus 1999. Head-driven statistical models for natural language parsing, University of Pennsylvania, Philadelphia, PA.

[8]

Demner-Fushman D, Apostolova E, Islamaj Doğan R, et al. 2010. NLM's system description for the fourth i2b2/VA challenge. In Proceedings of the 2010 i2b2/VA Workshop on Challenges in Natural Language Processing for Clinical Data. Boston, MA, USA: i2b2

[9]

Fan R. E., Chang K. W., Hsieh C. J., Wang X. R., Lin C. J. 2008. LIBLINEAR: a library for large linear classification. J Mach Learn Res 2008; 9:1871e4.

[10]

Doing-Harris K, Livnat Y and Meystre S. 2015. Automated concept and relationship extraction for the semi-automated ontology management (SEAM) system. Journal of Biomedical Semantics (2015) 6:15 DOI= http://doi.acm.org/10.1186/s13326-015-0011-7.

[11]

Friedman C, Alderson PO, Austin JHM, Cimino JJ, Johnson SB. 1994. A general natural-language text processor for clinical radiology. J Am Med Inform Assoc 1994; 1:161--74.

[12]

Grouin C, Abacha AB, Bernhard D, et al. 2010. CARAMBA: concept, assertion, and relation annotation using machine-learning based approaches. In Proceedings of the 2010 i2b2/VA Workshop on Challenges in Natural Language Processing for Clinical Data. Boston, MA, USA: i2b2, 2010

[13]

Henriksson A, Moen H, Skeppstedt M, Daudaravičius V, Duneld M. S. 2014. Synonym extraction and abbreviation expansion with ensembles of semantic spaces. J Biomed Semantics. 2014; 5: 6. Published online 2014 Feb 5. DOI= http://doi.acm.org/10.1186/2041-1480-5-6.

[14]

Manning, Christopher D., Surdeanu, Mihai, Bauer, John, Finkel, Jenny, Bethard, Steven J., and McClosky, David. 2014. The Stanford CoreNLP Natural Language Processing Toolkit. In Proceedings of 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp. 55--60.

[15]

Mark Hall, Eibe Frank, Geoffrey Holmes, Bernhard Pfahringer, Peter Reutemann, Ian H. Witten 2009. The WEKA Data Mining Software: An Update; SIGKDD Explorations, Volume 11, Issue 1.

[16]

Marie-Catherine de Marneffe, Bill MacCartney and Christopher D. Manning. 2006. Generating Typed Dependency Parses from Phrase Structure Parses. In LREC.

[17]

Marie-Catherine de Marneffe and Christopher D. Manning 2008. Stanford typed dependencies manual.

[18]

Patrick JD, Nguyen DHM, Wang Y, et al. 2010. I2b2 challenges in clinical natural language processing 2010. In Proceedings of the 2010 i2b2/VA Workshop on Challenges in Natural Language Processing for Clinical Data. Boston, MA, USA: i2b2, 2010

[19]

Rindflesch TC, Rayan JV, Hunter L. Extracting molecular binding relationships from biomedical text 2000. In: Nirenburg S. In Proceedings of the 6th Conference on Applied Natural Language Processing. Morristown, NJ, USA: Association for Computational Linguistics; p. 188--95.

Digital Library

[20]

Roberts K, Rink B, Harabagiu S. 2010. Extraction of medical concepts, assertions, and relations from discharge summaries for the fourth i2b2/VA shared task. Proceedings of the 2010 i2b2/VA Workshop on Challenges in Natural Language Processing for Clinical Data. Boston, MA, USA: i2b2, 2010

[21]

Savona G. K, Masanz J. J., Ogren P. V., Zheng J., Sohn S., Kipper-Schuler K. C., Chute C, 2010. Mayo clinical Text Analysis and Knowledge Extraction System (cTAKES): architecture, component evaluation and applications. J Am Med Inform Assoc. 2010 Sep--Oct; 17(5): 507--513. DOI=http://doi.acm.org/10.1136/jamia.2009.001560

[22]

Solt I, Szidarovszky FP, Tikk D. 2010. Concept, assertion and relation extraction at the 2010 i2b2 relation extraction challenge using parsing information and dictionaries. In Proceedings of the 2010 i2b2/VA Workshop on Challenges in Natural Language Processing for Clinical Data. Boston, MA, USA: i2b2, 2010

[23]

Uzuner O, South BR, Shen S, DuVall SL. 2010. i2b2/VA challenge on concepts, assertions, and relations in clinical text. J Am Med Inform Assoc 2011; 18:552e6

[24]

Universal Dependencies documentation. Retrieved 20 March, 2015, from Universal Dependencies contributors: http://universaldependencies.github.com/docs/

Cited By

Foufi VTimakum TGaudet-Blavignac CLovis CSong M(2018)Health social network analytics: utilizing social media to detect the outcome of chronic diseases (Preprint)Journal of Medical Internet Research10.2196/12876Online publication date: 22-Nov-2018
https://doi.org/10.2196/12876
Chen LLi YChen WLiu XYu ZZhang S(2018)Utilizing soft constraints to enhance medical relation extraction from the history of present illness in electronic medical recordsJournal of Biomedical Informatics10.1016/j.jbi.2018.09.01387(108-117)Online publication date: Nov-2018
https://doi.org/10.1016/j.jbi.2018.09.013
Budai KDinsoreanu MBarbantan IPotolea R(2016)Learning relations using semantic-based vector similarity2016 IEEE 12th International Conference on Intelligent Computer Communication and Processing (ICCP)10.1109/ICCP.2016.7737125(69-75)Online publication date: Sep-2016
https://doi.org/10.1109/ICCP.2016.7737125

Index Terms

REMed: automatic relation extraction from medical documents

Recommendations

Mining Text Using Keyword Distributions

Knowledge Discovery in Databases (KDD) focuses on the computerized exploration of large amounts of data and on the discovery of interesting patterns within them. While most work on KDD has been concerned with structured databases, there has been little work ...
Distributional Features for Text Categorization

Text categorization is the task of assigning predefined categories to natural language text. With the widely used 'bag of words' representation, previous researches usually assign a word with values such that whether this word appears in the document ...
Associative Feature Information Extraction Using Text Mining from Health Big Data

With the development of big data computing technology, most documents in various areas, including politics, economics, society, culture, life, and public health, have been digitalized. The structure of conventional documents differs according to their ...

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences

iiWAS '15: Proceedings of the 17th International Conference on Information Integration and Web-based Applications & Services

December 2015

704 pages

ISBN:9781450334914

DOI:10.1145/2837185

General Chair:
Gabriele Anderst-Kotsis
Johannes Kepler University Linz, Austria
,
Program Chair:
Maria Indrawan-Santiago
Monash University, Australia

Copyright © 2015 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]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 11 December 2015

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

iiWAS '15

iiWAS '15: The 17th International Conference on Information Integration and Web-based Application & Services

December 11 - 13, 2015

Brussels, Belgium

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

3
Total Citations
View Citations
145
Total Downloads

Downloads (Last 12 months)6
Downloads (Last 6 weeks)0

Reflects downloads up to 16 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Foufi VTimakum TGaudet-Blavignac CLovis CSong M(2018)Health social network analytics: utilizing social media to detect the outcome of chronic diseases (Preprint)Journal of Medical Internet Research10.2196/12876Online publication date: 22-Nov-2018
https://doi.org/10.2196/12876
Chen LLi YChen WLiu XYu ZZhang S(2018)Utilizing soft constraints to enhance medical relation extraction from the history of present illness in electronic medical recordsJournal of Biomedical Informatics10.1016/j.jbi.2018.09.01387(108-117)Online publication date: Nov-2018
https://doi.org/10.1016/j.jbi.2018.09.013
Budai KDinsoreanu MBarbantan IPotolea R(2016)Learning relations using semantic-based vector similarity2016 IEEE 12th International Conference on Intelligent Computer Communication and Processing (ICCP)10.1109/ICCP.2016.7737125(69-75)Online publication date: Sep-2016
https://doi.org/10.1109/ICCP.2016.7737125

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents