research-article

Signs and Symptoms Tagging for Thai Chief Complaints Based on ICD-10

Authors:

Jarunee DuangsuwanAuthors Info & Claims

ICACS '17: Proceedings of the 1st International Conference on Algorithms, Computing and Systems

Pages 44 - 49

https://doi.org/10.1145/3127942.3127957

Published: 10 August 2017 Publication History

Abstract

This paper presents a natural language processing (NLP) approach to construct signs and symptoms corpus in order to identify signs and symptoms recoded in a Thai chief complains (CCs) based on the International Statistical Classification of Diseases and Related Health Problems 10th Revision (ICD-10) form. We define our native language "Thai language" as the natural language in our works thus the challenge is how to apply NLP concept that is originally designed for English language. We start from tokenization to extract Thai token from Thai chief complains, and then the tokens is analyzed in order to assigning a specific tag in terms of ICD-10 code.

References

[1]

Wikipedia (19 January 2017) ICD-10, Available at: https://en.wikipedia.org/wiki/ICD-10#cite_note-WHOICD-1 (Accessed: 7 February 2017).

[2]

Wu, T. S. J., et al. 2008. Establishing a nationwide emergency department-based syndromic surveillance system for better public health responses in Taiwan. BMC public health 8(1), 18.

[3]

Lu, H. M., et al. 2009. Multilingual chief complaint classification for syndromic surveillance: An experiment with Chinese chief complaints. International Journal of Medical Informatics 78(5), 308--320.

[4]

Charnyapornpong, S. 1983. A Thai Syllable Separation Algorithm. Asian Institute of Technology.

[5]

Poonwarawan, Y. 1986. Dictionary-based Thai Syllable Separation. In proceeding of the 9th Electrical Engineering Conference.

[6]

Sornlertlamvanich, V. 1993. Word Segmentation for Thai in a Machine Translation System. NECTEC, Bangkok.

[7]

Haruechaiyasak, C., Kongyoung, S., & Dailey, M. 2008. A comparative study on thai word segmentation approaches. In Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, 2008. ECTI-CON 2008. 5th International Conference on (vol. 1, pp. 125--128). IEEE.

[8]

Mollá, D., and Santiago-Martinez, M. E. 2011. Development of a corpus for evidence based medicine summarisation. In Proceedings of the Australasian Language Technology Association Workshop.

[9]

Deleger, L., et al. 2012. Building gold standard corpora for medical natural language processing tasks. AMIA.

[10]

Sornlertlamvanich, V., Takahashi, N., & Isahara, H. 1999. Building a Thai part-of-speech tagged corpus (ORCHID). Journal of the Acoustical Society of Japan (E) 20(3), 189--198.

[11]

Theeramunkong, T., et al. 2010. Thai-nest: A framework for thai named entity tagging specification and tools. In Proc. of the 2nd Int'l Conference on Corpus Linguistics (CILC'10), 895--908.

[12]

Tongtep, N., and Theeramunkong, T. 2011 Multi-stage annotation using pattern-based and statistical-based techniques for automatic thai annotated corpus construction. In Proc. of the 9th Workshop on Asian Language Resources collocated with IJCNLP 2011, 50--58.

[13]

Jiang, M., Chen, Y., Liu, M., Rosenbloom, S. T., Mani, S., Denny, J. C., & Xu, H. 2011. A study of machine-learning-based approaches to extract clinical entities and their assertions from discharge summaries. Journal of the American Medical Informatics Association 18(5), 601--606.

[14]

Chen, Y., Lasko, T. A., Mei, Q., Denny, J. C., & Xu, H. 2015. A study of active learning methods for named entity recognition in clinical text. Journal of Biomedical Informatics 58, 11--18.

Digital Library

[15]

Song, M., Kim, W. C., Lee, D., Heo, G. E., & Kang, K. Y. 2015. PKDE4J: Entity and relation extraction for public knowledge discovery. Journal of Biomedical Informatics 57, 320--332.

Digital Library

[16]

Chanlekha, H. & Kawtrakul, A. 2004. Thai named entity extraction by incorporating maximum entropy model with simple heuristic information. In Proceedings of the IJCNLP.

[17]

Saeku, P. & Duangsuwan, J. 2016. ICD-10 Symptoms and Signs Identification from Thai Chief Complaints using Natural Language Processing. In Procceedings of the ICSEC.

[18]

Lexto, Available at: http://sansarn.com/

Cited By

Duangsuwan JSaeku P(2018)Semi-automatic Classification Based on ICD Code for Thai Text-Based Chief Complaint by Machine Learning TechniquesInternational Journal of Future Computer and Communication10.18178/ijfcc.2018.7.2.5177:2(37-41)Online publication date: Jun-2018
https://doi.org/10.18178/ijfcc.2018.7.2.517

Index Terms

Signs and Symptoms Tagging for Thai Chief Complaints Based on ICD-10
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Language resources

Recommendations

Enhancing aspects of Thai chief complaint classification Performance
ICCAE 2020: Proceedings of the 2020 12th International Conference on Computer and Automation Engineering

In this paper, we describe the aspects affecting in our experimental results of classifying Thai chief complaint (ThCC) into ICD-10 code. By merging our proposed Thai word separator to machine learning-based classifiers, ThCC have been converted into ...
Improving Accuracy in Thai Sign and Symptom Classification using Context-Free Grammar Approach
ICCAE 2018: Proceedings of the 2018 10th International Conference on Computer and Automation Engineering

We examine our proposed word separator for Thai script called two-level tokenization (2LT) by applying this tokenizer to medical Thai script including chief complaints, ICD-10 descriptions. We verify the results of tokenization through the machine ...
Using chief complaints for syndromic surveillance

Display Omitted We present a review of 15 chief complaint classification systems currently in use in North America.Classifiers can be divided into those that use statistical methods and those that use keyword based methods.All of the systems reviewed ...

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences

ICACS '17: Proceedings of the 1st International Conference on Algorithms, Computing and Systems

August 2017

117 pages

ISBN:9781450352840

DOI:10.1145/3127942

Copyright © 2017 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: 10 August 2017

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Conference

ICACS '17

ICACS '17: 2017 International Conference on Algorithms, Computing and Systems

August 10 - 13, 2017

Jeju Island, Republic of Korea

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

1
Total Citations
View Citations
83
Total Downloads

Downloads (Last 12 months)7
Downloads (Last 6 weeks)0

Reflects downloads up to 07 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

Duangsuwan JSaeku P(2018)Semi-automatic Classification Based on ICD Code for Thai Text-Based Chief Complaint by Machine Learning TechniquesInternational Journal of Future Computer and Communication10.18178/ijfcc.2018.7.2.5177:2(37-41)Online publication date: Jun-2018
https://doi.org/10.18178/ijfcc.2018.7.2.517

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.

Figures

Tables

Media

View Table of Conten