Knowledge map construction method for medical images

A technology of knowledge graph and construction method, which is applied in medical imaging, healthcare informatics, informatics, etc., can solve the problems of low knowledge acquisition rate, complex and diverse imaging data, etc., achieve low time complexity and improve knowledge acquisition efficiency and reduce knowledge redundancy

Active Publication Date: 2019-02-22
安徽影联云享医疗科技有限公司
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  • Abstract
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  • Claims
  • Application Information

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Problems solved by technology

[0006] Aiming at the problems of complex and diverse imaging data and low knowledge acquisition rate, the present invention provides a knowledge map construction method for medical imaging

Method used

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  • Knowledge map construction method for medical images
  • Knowledge map construction method for medical images
  • Knowledge map construction method for medical images

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Experimental program
Comparison scheme
Effect test

Embodiment 1

[0073] There are usually two construction methods for knowledge graphs: top-down (Top-Down) and bottom-up (Bottom-Up). The top-down method is to build ontology first, and match the extracted entities to the constructed top-level ontology; the bottom-up method is to directly extract the relationship between entities from the extracted data and update them into the knowledge graph . The present invention adopts a bottom-up method to construct a medical imaging diagnosis knowledge map, and the process is as follows figure 1 Shown:

[0074] A knowledge map construction method for medical imaging, the construction process includes:

[0075] (1) Knowledge representation Using the frame theory representation, all the data stored in the graph database constitute an entity relationship network and form a knowledge graph;

[0076] (2) Knowledge acquisition First extract entities, attributes, and attribute values, and then extract relationships between entities and entity attributes t...

Embodiment 2

[0082] The scheme of embodiment 2 is basically the same as that of embodiment 1. In the process (1) of embodiment 2, the knowledge representation uses "frame name-side-side name" as the basic expression method, and the specific representation process is as follows:

[0083] Connect the upper and lower frames with inheritance relationship through vertical connection, and use a frame name as the slot value or side value of a slot to establish the connection between frames through horizontal connection;

[0084] In the process of constructing the frame theory, it is accomplished in three ways: inheritance, matching and slot filling.

[0085] The specific steps of the framework to represent knowledge are:

[0086] (1) Analyze the knowledge objects and attributes of medical images in medical imaging textbooks and literature, set the slots and sides in the frame, set corresponding slots and sides for all possible attributes, and avoid expressing useless attributes .

[0087] (2) I...

Embodiment 3

[0104] The source of medical knowledge can be unstructured data such as textbooks and academic journals, semi-structured data such as Wikipedia and electronic medical records, or structured data such as databases. However, in the present invention, unstructured data such as textbooks and academic journals are used as knowledge sources, which can avoid the problem of low knowledge acquisition rate due to the diversity of data structures.

[0105] On the basis of Example 2, the unstructured data of Example 3 is obtained in the following three ways:

[0106] Method 1. Obtaining by means of rules and dictionaries;

[0107] The specific methods of obtaining unstructured data based on rules and dictionaries are as follows:

[0108] Obtain structured medical knowledge from unstructured text through regular expressions and forward maximum matching algorithm;

[0109] The specific process of obtaining structured medical knowledge through regular expressions and forward maximum matchi...

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Abstract

The invention discloses a knowledge map construction method for medical images, belonging to the field of knowledge maps. The construction process includes a step of knowledge representation with a framework theory representation method, a step of knowledge acquisition, wherein the knowledge sources of entity, attribute and attribute value extraction is unstructured data, a step of knowledge fusion with the integration of acquired new knowledge and the elimination of ambiguity, a step of knowledge processing with the knowledge reasoning and quality assessment of the data which is subjected tothe knowledge fusion and the adding of qualified data to a knowledge map, and a step of knowledge update with the update of the knowledge map according to the update and development of medical image knowledge. According to the characteristics of the medical image knowledge, and the knowledge acquisition rate is greatly improved with the unstructured data such as textbooks and academic journals asa source of knowledge.

Description

technical field [0001] The invention belongs to the field of knowledge graphs, and in particular relates to a method for constructing knowledge graphs for medical images. Background technique [0002] Knowledge graph is a cutting-edge research problem of intelligent big data. It conforms to the development of information age with its unique technical advantages; knowledge graph is a structured semantic knowledge base, a graph-based data structure, described in the form of symbols The concept of things and the relationship between them. In the medical field, a large amount of medical data has been accumulated. How to extract information from these data and manage, share and apply it is a key issue in promoting medical intelligence. The basis for intelligent processing of health records. [0003] Medical imaging is mainly used in artificial intelligence-assisted diagnosis to improve the accuracy of doctors' diagnosis of medical imaging. At present, there is no large-scale a...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G16H30/20
CPCG16H30/20
Inventor 李传富
Owner 安徽影联云享医疗科技有限公司
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