Classification Model for Medical Entity Relations with Convolutional Neural Network

[Objective] This paper proposes a new classification model for entity relationship based on the Convolutional Neural Network (CNN) with multi-features embedding, aiming to improve the classification results and simplify feature calculation. [Methods] Based on the existing algorithms of embedded features, our CNN model integrated word positions and lexical features, as well as demonstrated the representation methods for the features. These features did not require complex algorithm calculation, which improved the model’s performance. [Results] We examined the proposed model with the Bio-Medical corpus of AIMed, GENIA and ChemProt. The F1 scores were 0.7342, 0.9764 and 0.8900, respectively. This model yielded the best results with the GENIA and ChemProt datasets. [Limitations] Our model did not include the prior domain knowledge from biomedical field. [Conclusions] The proposed model could effectively conduct entity relationship classification, which also help the research on relation extraction and knowledgebase construction in bio-medical field. © 2021 Chinese Academy of Sciences. All rights reserved.