Residual network and attention mechanism-based drug relationship extraction method

Abstract

The invention discloses a residual network and attention mechanism-based drug relationship extraction method. The method comprises the following steps of: S1, carrying out vector representation on words in a drug entity relationship data set; S2, carrying out time-series modeling on a drug relationship statement by utilizing a two-layer bidirectional long-short term memory model neural network; S3, importing residual connection into the constructed two-layer bidirectional long-short term memory model neural network; S4, decomposing a deep semantic meaning automatically obtained by the two-layer bidirectional long-short term memory model neural network into a memory space and an attention space, fusing memory information and attention information, and inputting the fused information into aSoftmax classifier to extract a drug relationship. According to the drug relationship extraction method disclosed by the invention, dependency relationships between long-distance words are effectivelysolved, gradient diffusion is overcome, model overfitting is prevented, the model robustness is good and the classification effect is good.

Description

technical field [0001] The invention relates to the technical field of drug relationship extraction, in particular to a drug relationship extraction method based on a residual network and an attention mechanism. Background technique [0002] Drug relationship extraction means that when two drugs are taken at the same time, one drug will inhibit or stimulate the performance of the other drug. According to the research and investigation of Sahu et al. [1], more and more people choose to take multiple drugs at the same time, and the interaction between multiple drugs will cause harm to human health (arXiv preprintarXiv: 1701.08303, 2017). Therefore, in order to avoid drug abuse accidents, it is necessary to construct a data model reflecting drug interactions and design and develop a decision system for drug relationship extraction. At the same time, with the explosive growth of information in the biological field, there is an urgent need for an automatic way to collect and min...

AI Technical Summary

Problems solved by technology

However, similar to the previous feature-based relationship extraction system, the neural network relationship extraction model cannot highlight the importance of different drug entities in the relationship description and has poor robustness.

Images