應用文字探勘技術於疾病分類自動編碼之研究

劉姿蘭

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應用文字探勘技術於疾病分類自動編碼之研究

Using text mining for discharge summary classification

劉姿蘭 , Masters　　Advisor：王惠嘉　　

繁體中文

Naïve Bayes ；疾病分類；文字探勘； TF-IDF ； VSM ； SVM ； TF-IDF ； Naïve Bayes ； SVM ； VSM ； disease classification ； text mining

Abstract │ Reference (36) │ Altmetrics

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Taiwan has been in the era of National Health Insurance since March, 1995. Under this system, all the hospitals are supposed to arrange all the patients’ diagnosis records into International Classification of Diseases(ICD), and with this, they apply to the Bureau of National Health Insurance for the medical expenditure. Nowadays, disease classification staffs are employed to do the job in the medical centers, regional hospitals and local hospitals. They work with human labor, and spend a lot of time arranging the proper ICD. Due to too much time exhausted, too many cases to arrange, or lack of know-how or experience, mistakes or negligence are sometimes found.
  Accordingly, this research is made in the hope of studying how to classify the patients’ treatment data and their discharge summary in the six department of health insurance through the technology of text mining. Three prime methods, Naïve Bayes, SVM, and VSM are taken in the research; TF, TF-IDF and UMLS for alternation of synonyms are taken in it as well. The target is to find out the availability of auto classification in diseases and the diversities among the six departments. The possibility of promoting the accuracy of auto-classification prediction through TF, TF-IDF and alternation of synonyms is also the target.
  From the result of experiments, as far as the whole auto classification is concerned, it is suggested SVM without TF and TF-IDF be used. The accuracy rate of prediction in six departments of health insurance is 90.09% in maximum and 73.74% in minimum; the average is 79.37%. If the six departments are studied individually, it is found that VSM is suggested to be used in only one of the six departments, which TF-IDF is included with the setting threshold 0.1. Therefore, there are few differences in classification among the six departments of health insurance. However, the alternation of synonyms in UMLS is almost useless in promoting the accuracy rate of classification prediction. Moreover, in feature selection, TF-IDF is much better than TF in efficiency. On the whole, it is suggested that SVM be used as the method of auto classification in diseases.

Reference ( 36 ) 〈TOP〉

楊正銘. (民93). 以文字探勘技術應用於疾病分類之輔助系統 - 以出入院病歷摘要為例. 碩士論文, 台北醫學大學醫學資訊研究所.
連結：
李安唐. (民95). 電子病歷自動分類於ICD-9-CM. 碩士論文, 慈濟大學醫學資訊研究所.
連結：
Berman, J. J. (2004). Doublet method for very fast autocoding. BMC Medical Informatics and Decision Making, 4(16).
連結：
Bichindaritz, I. a. A., S. (2006). Concept mining for indexing medical literature. Engineering Applications of Artificial Intelligence, 19, 411-417.
連結：
Deogun, J. S., Sever, H. and Raghavan, V. V. (1998). Structural abstractions of hypertext documents for web-based retrieval. Proceeding of Ninth International Workshop on Database and Expert Systems Applications, 385-390.
連結：

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