| Summary: | 碩士 === 國立臺灣大學 === 生醫電子與資訊學研究所 === 107 === Our study aimed to construct a system for ICD-10 coding system, produced by supervised machine learning techniques, in order to categorize automatically free-text medical data using solely their content. There are numerous machine learning techniques and we use supervised machine learning to learn how to classify the ICD-10 codes from free-text data. At present, the work of classifying diseases mainly relies on manpower to read a large amount of written materials, such as discharge diagnosis, chief complaint, medical history, operation records and so on as the basis for classification. Coding is both laborious and time consuming. A disease coder with professional abilities also takes an average of 20 minutes, if we can provide an automatic code classification system with enough accuracy compared with professional coder, this model can significantly reduce the human labor in the code classification time.
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