Multichannel CNN Model for Biomedical Entity Reorganization.
Biomedical researchers and biologists often search a large amount of literature to find the relationship between biological entities, such as drug-drug and compound-protein. With the proliferation of medical literature and the development of deep learning, the automatic extraction of biological enti...
| Published in: | BioMed Research International pp. 1 - 12 |
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| Main Authors: | , , , , , |
| Format: | equations & formulas research tables/charts Journal Article |
| Published: |
Wiley-Blackwell
3/19/2022
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=155845622&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 155845622 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 3/19/2022 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 155845622 155845622 155845622 10.1155/2022/5765629 155845622 ppf: 1 ppct: 11 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Multichannel CNN Model for Biomedical Entity Reorganization. aug: au: Singh, Ajay Kumar Khan, Ihtiram Raza Khan, Shakir Pant, Kumud Debnath, Sandip Miah, Shahajan affil: Mody University of Science and Technology, India sug: subj: Research, Medical Information Retrieval Neural Networks (Computer) Utilization Data Quality Human Deep Learning Methods Semantics Data Mining ab: Biomedical researchers and biologists often search a large amount of literature to find the relationship between biological entities, such as drug-drug and compound-protein. With the proliferation of medical literature and the development of deep learning, the automatic extraction of biological entity interaction relationships from literature has shown great potential. The fundamental scope of this research is that the approach described in this research uses technologies like dynamic word vectors and multichannel convolution to learn a larger variety of relational expression semantics, allowing it to detect more entity connections. The extraction of biological entity relationships is the foundation for achieving intelligent medical care, which may increase the effectiveness of intelligent medical question answering and enhance the development of precision healthcare. In the past, deep learning methods have achieved specific results, but there are the following problems: the model uses static word vectors, which cannot distinguish polysemy; the weight of words is not considered, and the extraction effect of long sentences is poor; the integration of various models can improve the sample imbalance problem, the model is more complex. The purpose of this work is to create a global approach for eliminating different physical entity links, such that the model can effectively extract the interpretation of the expression relationship without having to develop characteristics manually. To this end, a deep multichannel CNN model (MC-CNN) based on the residual structure is proposed, generating dynamic word vectors through BERT (Bidirectional Encoder Representation from Transformers) to improve the accuracy of lexical semantic representation and uses multihead attention to capture the dependencies of long sentences and by designing the Ranking loss function to replace the multimodel ensemble to reduce the impact of sample imbalance. Tested on multiple datasets, the results show that the proposed method has good performance. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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