Open AccessStudy of Data Representation Methods for TCM Clinical Assistant Diagnosis
Author(s) -
Liu Yin,
Hongguang Chen
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1550/3/032028
Subject(s) - task (project management) , computer science , representation (politics) , artificial intelligence , perceptron , class (philosophy) , external data representation , clinical diagnosis , machine learning , medical diagnosis , natural language processing , data mining , artificial neural network , medicine , pathology , engineering , clinical psychology , systems engineering , politics , political science , law
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.