Automatic ICD-10 classification from free-text - a paper from National Taiwan University

This model can provide disease coders hints in classification work to help them with the first few codes and thereby speed up their classification. The proposed method can classify the first three digits with an f-measure of 0.7, and the goal of future work is to improve the results sufficiently to replace disease coders. By using word2vec and the neural network, computers can understand free-text data that can only be read by humans. Computer can learn the semantics underlying the language and help humans perform the otherwise laborious work. In this the proposed method, the input data are written in English. However, word2vec can transform all types of language into word vectors. Thus, hospitals in other countries can use this method to classify their free-text medical data using ICD-10 codes as well. 

Main paper http://research.ord.ntu.edu.tw/landscape/inner.aspx?id=226&chk=af48bff2-47dc-4a5c-b103-455182c16259

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