Abstract
The unbalanced distribution of medical resources renders the research on TCM (Traditional Chinese Medicine) clinical assistant diagnosis more important to regions with less medical resources. In recent years, more and more clinical assistant diagnosis methods are deep learning (DL) based. The input data representation of these DL models is one of the most important factors for achieving better results. In this paper, different data representations methods are investigated using a multi-layer perceptron for a multi-class multi-label TCM clinical assistant diagnosis task. From the experimental results, it can be concluded that fast-Text representation is more suitable to this task since TCM clinical records are brief with limited information.
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