Mammography report semantic tree model building method supporting heterogeneous information integration

A heterogeneous information and semantic tree technology, which is applied in the fields of semantic tool creation, informatics, medical reports, etc., can solve the problems of poor portability of Chinese medical texts, few researches on mammography diagnosis report text structure, natural language computer Can not directly identify and deal with problems such as

Pending Publication Date: 2019-08-02
DONGHUA UNIV
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Problems solved by technology

Nowadays, electronic medical records have also replaced paper medical records, but what remains unchanged is that the patient's diagnosis report is still an unstructured description of the relevant parts based on the doctor's knowledge and work experience with the help of natural language, but natural language cannot be directly recognized by computers. with processing
[0003] Text structured processing is the key to the development of artificial intelligence in the medical field. Foreign

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  • Mammography report semantic tree model building method supporting heterogeneous information integration
  • Mammography report semantic tree model building method supporting heterogeneous information integration
  • Mammography report semantic tree model building method supporting heterogeneous information integration

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Embodiment Construction

[0016] In order to make the present invention more comprehensible, preferred embodiments are described in detail below with accompanying drawings.

[0017] A method for establishing a mammography report semantic tree model supporting heterogeneous information integration provided by the present invention includes constructing a relevant semantic tree for the text description of mammography images expressed in the real world in a medical situation. The construction of the semantic tree of mammography images in Chinese mainly includes the following steps: forming a database of text descriptions of mammography images in breast cancer according to expert rules; The word segmentation work of the division method; use the above features to carry out semantic constraints on the entities in the text description of breast cancer mammography images; according to the semantic constraints, connect the nodes with relationships in the semantic tree to form a complete breast cancer mammography...

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Abstract

The invention relates to a mammography report semantic tree model building method supporting heterogeneous information integration. The mammography report semantic tree model building method supporting heterogeneous information integration is characterized by comprising the following steps: forming a text normalization database of breast cancer molybdenum target image expression text description;obtaining text description expressed by the breast cancer molybdenum target image in real time, and performing phrase division on the text description based on the text normalization database according to semantic information; obtaining semantic constraints of the entity; and forming a semantic tree of text description. According to the mammography report semantic tree model building method supporting heterogeneous information integration, structuralization of text information of complex breast cancer molybdenum target images from different hospitals and different doctors can be achieved by constructing a mammography semantic tree, and semantic-based integration of heterogeneous information is realized.

Description

technical field [0001] The invention relates to a method for establishing a mammography report semantic tree model supporting heterogeneous information integration, and belongs to the field of medical text structured processing. Background technique [0002] With the rapid development of medical informatization, 80% of hospitals have completed the construction of informatization services. Nowadays, electronic medical records have also replaced paper medical records, but what remains unchanged is that the patient's diagnosis report is still an unstructured description of the relevant parts based on the doctor's knowledge and work experience with the help of natural language, but natural language cannot be directly recognized by computers. with processing. [0003] Text structured processing is the key to the development of artificial intelligence in the medical field. Foreign natural language processing systems such as MedLEE (Medical Language Extraction and Encoding System)...

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Application Information

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IPC IPC(8): G16H15/00G06F16/31G06F16/34G06F16/36
CPCG16H15/00G06F16/34G06F16/322G06F16/36
Inventor 李继云孙莉黄鹏顾莹莹李凯华乐嘉锦
Owner DONGHUA UNIV
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