Drug interaction relationship extraction method and system based on external knowledge

A technology of relation extraction and external knowledge, applied in neural learning methods, special data processing applications, biological neural network models, etc., can solve problems such as large differences, low recall rate, and large impact on extraction performance.

Active Publication Date: 2020-10-23
SICHUAN UNIV
View PDF8 Cites 5 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

In the rule-based method, the formulation of the rules generally requires the assistance of professionals in the medical field. Due to the diversity of language expressions, the formulated rules are often difficult to cover all drug interaction relationships, so the recall rate of this method is low; Traditional machine learning methods usually need to use a large number of manually defined features, such as parts of speech, syntax, grammar, etc., and need to use external natural language processing tools to generate thes

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
  • Drug interaction relationship extraction method and system based on external knowledge
  • Drug interaction relationship extraction method and system based on external knowledge
  • Drug interaction relationship extraction method and system based on external knowledge

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0068] refer to figure 1 , is a schematic structural diagram of a drug interaction relationship extraction system based on external knowledge in the present invention, specifically, a drug interaction relationship extraction system based on external knowledge, including:

[0069] The drug information data set building block 1 is used to analyze and process the content of the drug database, extract and generate interacting drug pairs, and at the same time retain the description information of all drugs to form a drug interaction data set with drug description information;

[0070] In this embodiment, a commonly used drug database can be used, such as the DrugBank database, which contains related information such as drug type, drug structure, drug description information, and other drugs that interact with the drug;

[0071] In this embodiment, the drug information data set construction module 1 finds that the description information of the drug is a detailed introduction to the...

Embodiment 2

[0100] Based on the system of embodiment 1, this embodiment provides a method for extracting drug interaction relationship based on external knowledge, refer to image 3 , is a schematic flow chart of the method, specifically: a method for extracting drug interaction relationships based on external knowledge, including the following steps:

[0101] S400: Analyze and process the content of the drug database, extract and generate interacting drug pairs, and save all drug description information at the same time to form a drug interaction data set with drug description information; then perform step S402;

[0102] In this embodiment, commonly used drug databases, such as the DrugBank database, contain relevant information such as drug types, drug structures, drug description information and other drugs that interact with the drug; through the drug database DrugBank The knowledge is analyzed and processed, and drug pairs with interactions are extracted from them, and drug pairs wi...

Embodiment 3

[0125] In this example, the validity of the system of Example 1 and the method of Example 2 is verified, and the DDIExtraction2013 data set is used for experiments. This data set is composed of 792 medical texts in DrugBank and 233 abstracts in MedLine Composition, and the drug entity is marked in advance, the detailed information of the data set is shown in Table 1:

[0126] Table 1 DDIExtraction2013 data description

[0127] type of drug interaction Training set test set total Advice 826 221 1047 Effect 1687 360 2047 Mechanism 1319 302 1621 Int 188 96 284 Negative 23772 4737 28509

[0128] The length of the drug description information in this chapter is set to 50, and the word is converted into a 300-dimensional Glove word vector. For words that do not appear in the Glove vocabulary, the word vector of the word is randomly initialized. The number of iterations of the capsule network Set to 3, Batchsize to 64, drop...

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 provides a drug interaction relationship extraction method and system based on external knowledge, and the method comprises the following steps: carrying out the analysis and processingof the content of a drug database, extracting and generating interacted drug pairs, storing all drug description information, and forming a drug interaction data set with the drug description information; constructing a drug description system information training model, and performing training through the drug interaction data set to obtain and store an optimal model; combining the optimal modelwith a BiLSTM-Att-CapsNet model, so that an EK-BiLSTM-Att-CapsNet model can be obtained, meanwhile, drug entities of the drug interaction data set are recognized, corresponding drug description information is found in a drug database and stored, and finally, the combined model is trained to obtain a final relationship extraction model. According to the method, the problem of large difference of extraction results of different relationship categories can be relieved, and the extraction effect is improved.

Description

technical field [0001] The invention belongs to the technical field of natural language processing, and in particular relates to a method for extracting drug interaction relations based on external knowledge. Background technique [0002] Drug-Drug Interactions (DDI) refers to the synergistic or antagonistic effects between different drugs when patients take multiple drugs at the same time, which may cause side effects, resulting in increased treatment costs and harm to patients. Therefore, understanding the interaction knowledge between drugs is of great significance and value for the diagnosis and treatment of patients and the development of medicine. [0003] At present, the application methods in the field of drug interaction relationship extraction mainly include: rule-based methods, traditional machine learning-based methods and deep learning-based methods. In the rule-based method, the formulation of the rules generally requires the assistance of professionals in the...

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
IPC IPC(8): G06F40/279G06N3/04G06N3/08G06F16/335
CPCG06F40/279G06N3/049G06N3/08G06F16/335G06N3/045
Inventor 琚生根罗莘涛刘宁宁
Owner SICHUAN UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

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

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap