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Method for automatically labeling entities in medical text

An automatic labeling, entity technology, applied in medical informatics, medical data mining, informatics and other directions, can solve problems such as a lot of manpower, material resources, less entity types, errors, etc.

Pending Publication Date: 2019-10-18
北京百奥知信息科技有限公司
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Problems solved by technology

[0002] In the field of medical named entity recognition, the technology used is generally to train the model, and then use the model to label the entity. Most of the data used in the training model is obtained through manual labeling, which requires a lot of manpower and material resources. There are fewer entity types, the number of overall labels is small, and some errors will also occur. Therefore, an automatic labeling method is needed to solve this problem, making the labeling process more intelligent and simple, and making the labeling results more accurate.

Method used

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  • Method for automatically labeling entities in medical text

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Embodiment Construction

[0016] The present invention will be further described below in combination with specific embodiments and accompanying drawings.

[0017] Such as figure 1 As shown, a method for automatically labeling entities in medical texts, the method includes the processing method for the automatic labeling process of medical entities, and the combined method for processing multiple entities, nested entities, etc. The corpus marked by the automatic labeling method is a medical corpus, and the labeling results are more biased towards medical entities, such as diseases, drugs, adverse reactions, genes, and the like. The combined method can effectively solve the problems of plural entities, nested entities, and unrecognizable entities due to irregular punctuation marks, and improves the accuracy of labeling.

[0018] Its method will consist of text collection, text preprocessing, dictionary data collection, dictionary construction, text annotation, and annotation post-processing.

[0019] ...

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Abstract

The invention discloses a method for automatically labeling entities in a medical text. The method aims to correctly label entities in a text and comprises the following steps: collecting the text; preprocessing the text; collecting entity dictionary data; preprocessing the dictionary data to generate an abbreviation dictionary, a phrase dictionary and other entity dictionaries; labeling the textdata by applying a labeling rule to obtain a labeling entity; and labeling post-processing: processing a labeling result by adopting a fuzzy recognition mode, adding entities with missed recognition,removing nested entities, and finally obtaining a final labeling result.

Description

technical field [0001] The invention belongs to the technical field of medical entity labeling, in particular to a method for automatically labeling entities in medical texts. Background technique [0002] In the field of medical named entity recognition, the technology used is generally to train the model, and then use the model to label the entity. Most of the data used in the training model is obtained through manual labeling, which requires a lot of manpower and material resources. There are fewer entity types, the number of overall annotations is small, and some errors will also occur. Therefore, an automatic labeling method is needed to solve this problem, making the labeling process more intelligent and simple, and making the labeling results more accurate. Contents of the invention [0003] In view of this, the technical problem to be solved by the present invention is a method for annotating entities in medical corpus, which can automatically annotate entities and...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/27G16H50/70
CPCG16H50/70G06F40/205G06F40/279G06F40/295G06F40/289
Inventor 管仁初刘洪涛张浩贺宝润周丰丰
Owner 北京百奥知信息科技有限公司
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