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A drug relationship extraction method based on deep learning

A technology of relation extraction and deep learning, applied in instrumentation, unstructured text data retrieval, molecular design, etc., can solve problems such as difficulty in covering all text scenarios, relying on external natural language processing tools, etc., to reduce learning cost and learning burden , Improve the extraction accuracy and reduce the effect of potential risks

Active Publication Date: 2022-05-31
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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Problems solved by technology

[0006] Aiming at the above-mentioned deficiencies in the prior art, a drug relationship extraction method based on deep learning provided by the present invention solves the problem that existing methods are difficult to cover all text scenes and rely too much on external natural language processing tools

Method used

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  • A drug relationship extraction method based on deep learning
  • A drug relationship extraction method based on deep learning
  • A drug relationship extraction method based on deep learning

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Embodiment Construction

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[0091] In one embodiment of the present invention, drug literature can be obtained from PubMed. Bidirectional Long Short-Term Memory Model

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Abstract

The invention discloses a drug relationship extraction method based on deep learning. The invention uses the RDKit tool to convert the drug molecular formula into a molecular graph structure, and then expresses the characteristics of the drug molecule, and at the same time extracts the text features of the sample, and extracts the drug molecule After the feature and the text feature of the sample are combined, the fully connected layer softmax is used to classify the drug relationship, and the physical and chemical properties of the drug in the sentence are used, which can improve the extraction accuracy and solve the problem that the existing methods are difficult to cover all text scenes and rely too much on Problems with external natural language processing tools.

Description

A deep learning-based drug relationship extraction method technical field The present invention relates to the field of medicinal chemistry entity relationship extraction, in particular to a kind of drug relationship based on deep learning extraction method. Background technique Medicinal chemical entity relationship extraction refers to the automatic extraction of the relationship between drug entities from the text, which can assist in the extraction of drug entities. It also helps medical researchers to develop new drugs, assist doctors to formulate reasonable treatment plans for patients, and build knowledge of medicinal chemistry. The foundation of the database. Existing methods for extracting the interaction relationship between medicinal and chemical entities mainly fall into two categories: rule-based methods and rule-based methods. There are methods for supervised machine learning. [0003] In the early stage of the research, rule-based methods were mostly ...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F16/35G06F40/211G06K9/62G16C20/50
CPCG06F16/35G06F40/211G16C20/50G06F18/2415
Inventor 刘勇国何家欢杨尚明李巧勤
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA
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