Concepts encoding via knowledge-guided self-attention networks

Kunnan Geng; Xin Li; Wenyao Zhang

doi:10.1117/12.2644388

12 October 2022 Concepts encoding via knowledge-guided self-attention networks

Kunnan Geng, Xin Li, Wenyao Zhang

Proceedings Volume 12342, Fourteenth International Conference on Digital Image Processing (ICDIP 2022); 123423T (2022) https://doi.org/10.1117/12.2644388
Event: Fourteenth International Conference on Digital Image Processing (ICDIP 2022), 2022, Wuhan, China

Abstract

With the growth of digital data created by us, a large number of deep learning models have been proposed for data mining. Representation learning offers an exciting avenue to address data mining demands by embedding data into feature space. In the healthcare field, most existing methods are proposed to mine electronic health records (EHR) data by learning medical concept representations. Despite the vigorous development of this field, we find the contextual information of medical concepts has always been overlooked, which is important to represent these concepts. Given these limitations, we design a novel medical concept representation method, which is equipped with a self-attention mechanism to learn contextual representation from EHR data and prior knowledge. Extensive experiments on medication recommendation tasks verify the designed modules are consistently beneficial to model performance.

Citation Download Citation

Kunnan Geng, Xin Li, and Wenyao Zhang "Concepts encoding via knowledge-guided self-attention networks", Proc. SPIE 12342, Fourteenth International Conference on Digital Image Processing (ICDIP 2022), 123423T (12 October 2022); https://doi.org/10.1117/12.2644388

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