Selected article for: "attention mechanism and deep learning model"
Author: Liu, Yuliang; Zhang, Quan; Zhao, Geng; Liu, Guohua; Liu, Zhiang
Title: Deep Learning-Based Method of Diagnosing Hyperlipidemia and Providing Diagnostic Markers Automatically
  • Document date: 2020_3_11
    • ID: 1r4gm2d4_7
    Snippet: The deep learning model used in this paper is the LSTM network which combined with the attention mechanism. The eigenvector composed of human hematological parameters is fed to the LSTM layer after it was processed by the attention layer. The LSTM layer can extract the joint features hidden in the original data automatically. Finally, the extracted joint features are processed by the classification function to achieve the purpose of the automatic.....
    Document: The deep learning model used in this paper is the LSTM network which combined with the attention mechanism. The eigenvector composed of human hematological parameters is fed to the LSTM layer after it was processed by the attention layer. The LSTM layer can extract the joint features hidden in the original data automatically. Finally, the extracted joint features are processed by the classification function to achieve the purpose of the automatic classification of samples. From the attention layer, we can know which physiological parameters play a decisive role in the diagnosis of the disease, and we can get the influence degree of different physiological parameters on the disease. The global parameter of the model was updated by Adam algorithm, 35 and as it is a binary classification task, the sigmoid function was used as a classification function.

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