Traditional Chinese medicine medical case naming identification method and system based on multi-feature template correction

Abstract

The invention discloses a traditional Chinese medicine medical case naming identification method and system based on multi-feature template correction. The method comprises the following steps that: carrying out sentence extraction on a traditional Chinese medicine medical case text; classifying extracted sentences; carrying out word segmentation processing on each category of sentences; carryingout character feature, part of speech feature, left demonstrative word feature, right demonstrative word feature and term feature annotation on each word obtained by word segmentation in sequence; constructing training corpora; formulating a feature template; independently inputting the obtained corpora and the feature template into a conditional random field model, and training the conditional random field model to obtain the trained conditional random field model; constructing corpora to be predicted for a traditional Chinese medicine medical case to be predicted; taking constructed identification corpora as an input, inputting into the trained conditional random field model, and outputting a traditional Chinese medicine medical case category and a character position; and finally, according to the traditional Chinese medicine medical case category and the character position, identifying the four diagnostic methods, the pattern of syndrome and the therapeutic method of the traditionalChinese medicine.

Description

technical field [0001] The invention relates to a naming recognition method and system for traditional Chinese medicine medical records based on multi-feature template correction. Background technique [0002] The key step in the informatization of TCM medical record texts is to be able to recognize the named entities of massive TCM medical record texts, and to establish the correlation between TCM text information, syndrome type text information, and treatment method text information. Scientifically demonstrate the idea of ​​"treatment based on syndrome differentiation". Named entity recognition has been widely used in many fields, such as: financial field, product recognition, Weibo text, military text, etc. [0003] Named entity recognition analysis methods can be roughly divided into two categories: [0004] The first category is rule-based and dictionary-based methods and machine learning-based methods. The disadvantage of the method based on rules and dictionaries i...

AI Technical Summary

Problems solved by technology

The disadvantage of the method based on rules and dictionaries is that it relies heavily on dictionaries and rule bases, and the ability to recognize unregistered words will be greatly reduced;

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