A drug-disease relationship classification method based on a neural network

A neural network, relation classification technology, applied in the fields of biomedical text mining and data mining

Active Publication Date: 2019-03-08
DALIAN JIAOTONG UNIVERSITY
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Problems solved by technology

[0006] The purpose of the present invention is to provide a neural network-based drug-disease relationship classification method, combined with domain knowledge, to automatically learn the chapter-level feat

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  • A drug-disease relationship classification method based on a neural network
  • A drug-disease relationship classification method based on a neural network
  • A drug-disease relationship classification method based on a neural network

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Embodiment

[0055] 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.

[0056] This embodiment uses the corpus provided by task 2 to identify drug-induced disease (chemical-induced disease, CID) relationship in the CDR (drug-disease relationship) challenge proposed by BioCreative V in 2015. The CDR corpus has annotated 1,500 articles, including 4,409 drugs, 5,818 diseases, and 3,116 drug-disease relationships at the conceptual level. It is currently the largest drug-disease relationship dataset. The CDR corpus contains a total of 1500 Medline articles including abstracts and titles only, and 500 articles for each data set in the training set, development set, and test set. During the experiment, the initial training set and development set were combined to expand the training data set. The instances in the merged union are randomly divided into 10 equal s...

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Abstract

The present invention relates to a drug-disease relationship classification method based on a neural network, which belongs to the technical field of biomedical text mining and data mining, and solvesthe problem of more accurate and effective classification of drug-induced relationship among drug-induced diseases for the drug-induced entities labeled in biomedical literature. The method comprisesS1 constructing a drug-induced relationship candidate case set; 2 performing text processing on that biomedical literature; 3 constructing the domain knowledge; 4 constructing an input vector; 5 constructing a text-level semantic information sub-network model; S6 adopting an attention mechanism to form the final representation of knowledge; S7 constructing a drug-disease relationship classification model; S8 predicting drug-disease relationships in the biomedical literature. The method can automatically identify the entity relationship of drug diseases between sentences and within sentences effectively, and overcomes the existing methods that most systems utilize a large number of feature engineering methods based on the traditional machine learning methods.

Description

technical field [0001] The invention relates to the technical fields of biomedical text mining and data mining, in particular to a neural network-based drug-disease relationship classification method. Background technique [0002] Massive unstructured biomedical literature contains rich, cutting-edge and potential biomedical knowledge, and is an important source of knowledge for practitioners in the field of biomedicine. It is very urgent to apply text mining technology to automatically and efficiently extract relevant knowledge from this knowledge repository. Drugs, diseases, and their relationships are among the most searched topics by PubMed users worldwide, reflecting their centrality in biomedical fields such as drug discovery and safety alerts, as well as in healthcare. Drugs and diseases have multiple relationships, such as the therapeutic relationship. In addition, drugs and diseases often have the following two relationships. One is the putative mechanism relatio...

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Application Information

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IPC IPC(8): G06F16/36G06F16/35G16H70/40
CPCG16H70/40
Inventor 郑巍林鸿飞
Owner DALIAN JIAOTONG UNIVERSITY
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