Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Bio-medical entity relation classification method combining attention mechanism and neural network

A biomedical and entity-relationship technology, applied in computer components, instruments, calculations, etc., can solve the problem of unsatisfactory interactive classification performance of biomedical entities

Inactive Publication Date: 2018-11-23
DALIAN UNIV OF TECH
View PDF3 Cites 13 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Although the above-mentioned different text mining methods have explored various methods to classify the interactive relationship between biomedical entities, the performance of interactive classification of biomedical entities with mostly long and complex sentences is not very satisfactory.

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Bio-medical entity relation classification method combining attention mechanism and neural network
  • Bio-medical entity relation classification method combining attention mechanism and neural network
  • Bio-medical entity relation classification method combining attention mechanism and neural network

Examples

Experimental program
Comparison scheme
Effect test

Embodiment

[0043] Based on the above description of specific implementations of the method and system involved in the present invention, description will be made in conjunction with specific embodiments.

[0044] This embodiment uses the DrugBank and Medline data sets in the DDIExtraction 2013 evaluation task, which are further divided into training sets and test sets. The DrugBank training and test sets contain 31270 and 1221 sentences, respectively, which also represent sentences in biomedical databases and sentences in biomedical articles, respectively. During the experiment, the training data sets on the two data sets were merged as the training set, and the two test sets were kept unchanged and the union of the two test sets Overall was used in the test.

[0045] A biomedical entity relationship classification method that combines the attention mechanism and the neural network. The specific steps are as follows:

[0046] 1. Text processing based on anaphora resolution: Collect the ...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention discloses a bio-medical entity relation classification method combining an attention mechanism and a neural network, which belongs to the technical field of biomedicine and data mining,and is used for solving the problem of bio-medical entity relation classification. According to the key points, the method comprises the steps of S1, performing text processing based on reference analysis; S2, constructing a model input vector based on the attention mechanism; S3, building a bio-medical entity relation classification model based on a bidirectional LSTM; and S4, carrying out bio-medical entity relation classification by utilizing the relation classification model. According to the method, following-based reference analysis is designed for sentences in biological literatures; then starting from basic unit words forming the sentences, embedding vectors of the words are weighted by utilizing the attention mechanism; weights of keywords having an important influence on bio-medical entity relation classification are highlighted, so that a relation between candidate entities is clearer; and the bio-medical entity relation classification is carried out.

Description

technical field [0001] The invention relates to the technical fields of biomedicine and data mining, in particular to a biomedical entity relationship classification method combining an attention mechanism and a neural network. Background technique [0002] With the development of data-driven bioinformatics, it has become a trend to discover and predict the relationship between biomedical entities through computational methods. Computational-based text mining methods can discover patterns and knowledge from the large number of available biological databases and unstructured texts. Currently, vast amounts of up-to-date unstructured data are hidden in specialized databases or scientific literature. Therefore, it is an effective and feasible way to detect and predict the relationship between biomedical entities from documents and databases using text mining technology. In addition, this can also automate the process of database annotation that is done manually, and also facil...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62
CPCG06F18/28G06F18/24G06F18/214
Inventor 林鸿飞郑巍
Owner DALIAN UNIV OF TECH
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com