ABSTRACT
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 ICD-10 code which stands for International Classification of Diseases, Tenth Revision, and is a standard code used by physicians and other healthcare professionals to identify all diagnoses, signs and symptoms. At the beginning of experiments, the dataset from the sign and symptom description ranged in group R00 to R69 of ICD-10 have been used for training the classifiers. Subsequently the classifiers have been applied to the test dataset represented by 150 chief complaint cases in order to assign the related ICD-10 codes, and to evaluate classification accuracy. The experiment achieves 85% precision, 76% F1-measure, and 71% recall using our proposed Thai word separator with Classification and Regression Trees (CART) technique. However, we need to increase the precision which is strong enough to support our proposed separator. The additional experiment has been done by adding 50 chief complaint cases to the test dataset. We also have applied our proposed techniques including conflict element finding and classification criteria setting to improve the precision. Consequently, the later experimental results get higher classification accuracy by decreasing the false positives to mitigate the low recall problem.
- Yuen Poowarawan.1986. Dictionary-based Thai Syllable Separation. In Proceedings of the Ninth Electronics Engineering Conference.Google Scholar
- Aroonmanakun, Wirote. 2002. Collocation and Thai Word Segmentation. In Proceedings of SNLP-Oriental COCOSDA.Google Scholar
- Software: SWATH - Thai Word Segmentation, http://www.cs.cmu.edu/~paisarn/software.htmlGoogle Scholar
- P. Saeku and J. Duangsuwan. 2017. Signs and Symptoms Tagging for Thai Chief Complaints Based on ICD-10. In Proceedings of the International Conference on Algorithms, Computing and Systems (ICACS '17). ACM, New York, NY, USA, 44--49. DOI: https://doi.org/10.1145/3127942.3127957Google Scholar
- J. Duangsuwan and P. Saeku. 2018. Semi-automatic classification based on ICD code for Thai text-based chief complaint by machine learning techniques, International J. Future Computer and Communication.Google Scholar
- Lavergne, Thomas, Aurélie Névéol, Aude Robert, Cyril Grouin, Grégoire Rey and Pierre Zweigenbaum.2016. A Dataset for ICD-10 Coding of Death Certificates: Creation and Usage. In Proceedings of BioTxtM@COLING 2016.Google Scholar
- Bevan Koopman, Guido Zuccon, Anthony Nguyen, Anton Bergheim, and Narelle Grayson. 2015b. Automatic ICD-10 classification of cancers from free-text death certificates. Int J Med Inform, 84(11):956--965, November.Google ScholarCross Ref
- Bevan Koopman, Sarvnaz Karimi, Anthony Nguyen, Rhydwyn McGuire, David Muscatello, Madonna Kemp, Donna Truran, Ming Zhang, and Sarah Thackway. 2015a. Automatic classification of diseases from free-textdeath certificates for real-time surveillance. In Proceedings of BMC Med Inform Decis Mak.Google Scholar
- W.W. Chapman, L.M. Christensen, M.M. Wagner, P.J. Haug, O. Ivanov, J.N. Dowling, et al. 2005. Classifying free-text triage chief complaints into syndromic categories with natural language processing. J. Artif. Intell. Med.Google Scholar
- R.T. Olszewski. 2003. Bayesian classification of triage diagnoses for the early detection of epidemics. In Proceedings of FLAIRS Conference.Google Scholar
- J.U. Espino, J. Dowling, J. Levander, P. Sutovsky, M.M. Wagner, G.F. Copper. 2006. SyCo: a probabilistic machine learning method for classifying chief complaints int. symptom and syndrome categories.. In Proceedings of Syndromic Surveillance Conference.Google Scholar
- ThaiNurseClub, "Patient Interviewing & History Taking," 2013. [Online]. Available: http://thainurseclub.blogspot.com.Google Scholar
Index Terms
- Enhancing aspects of Thai chief complaint classification Performance
Recommendations
Signs and Symptoms Tagging for Thai Chief Complaints Based on ICD-10
ICACS '17: Proceedings of the 1st International Conference on Algorithms, Computing and SystemsThis 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 ...
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 EngineeringWe 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 ...
Evaluation of preprocessing techniques for chief complaint classification
Objective:: To determine whether preprocessing chief complaints before automatically classifying them into syndromic categories improves classification performance. Methods:: We preprocessed chief complaints using two preprocessors (CCP and EMT-P) and ...
Comments