Second order relation graph embedding neural network for rational drug use prediction

Zhonghui Deng; Hai Yuan; Wei Feng; Zheng Xu; Jian Wang; Weijun Wang

doi:10.1117/12.2668194

20 April 2023 Second order relation graph embedding neural network for rational drug use prediction

Zhonghui Deng, Hai Yuan, Wei Feng, Zheng Xu, Jian Wang, Weijun Wang

Proceedings Volume 12602, International Conference on Electronic Information Engineering and Computer Science (EIECS 2022); 1260230 (2023) https://doi.org/10.1117/12.2668194
Event: International Conference on Electronic Information Engineering and Computer Science (EIECS 2022), 2022, Changchun, China

Abstract

Rational drug use prediction, which is used to estimate whether the drug use is reasonable in clinical medical treatment, is of great significance in combating excessive medical treatment. The feature interaction model based on electronic medical records is widely used in the field of medical behavior prediction. However, the features recorded in electronic medical records are very complex, and medical behavior has an obvious long-tail effect. The problem of feature sparseness makes traditional prediction models based on feature interaction impossible. It works well, we propose a novel business domain-based second-order relational graph embedding neural network model (SORGE-NN), which can distinguish the scope of features and mine the implicit propagation relationship, through the residual-multi-head attention layer , and perform high-order weighted combination of features, which effectively alleviates the challenges brought by feature sparse and complex features. We conduct experiments with real datasets, and the experimental results show that our proposed SORGE-NN achieves better results than current state-of-the-art prediction models.

Citation Download Citation

Zhonghui Deng, Hai Yuan, Wei Feng, Zheng Xu, Jian Wang, and Weijun Wang "Second order relation graph embedding neural network for rational drug use prediction", Proc. SPIE 12602, International Conference on Electronic Information Engineering and Computer Science (EIECS 2022), 1260230 (20 April 2023); https://doi.org/10.1117/12.2668194

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