Medical data classification method and device based on machine learning and computer equipment

Abstract

The invention relates to a medical data classification method and device based on machine learning and computer equipment. The method comprises the steps that a medical data classification request sent by an end is received, wherein the medical data classification request comprises case history information; the case history information is subjected to word separating processing to obtain a plurality of text vectors; the multiple text vectors are subjected to feature extraction to obtain a plurality of text vectors and corresponding feature dimension values; a target classifier is obtained based on training of multiple medical data, the multiple text vectors and the corresponding feature dimension values are subjected to traversal computation through a plurality of neural network nodes of the target classifier until the target nodes corresponding to the multiple text vectors are traversed, the type possibility corresponding to the multiple text vectors is calculated according to the target nodes, and the type result corresponding to the case history information is acquired according to the type possibility; and the type result corresponding to the case history information is pushedto the end. The medical data classification precision can be effectively improved by adopting the method.

Description

technical field [0001] The present application relates to the field of computer technology, in particular to a machine learning-based medical data classification method, device and computer equipment. Background technique [0002] In recent years, the prevalence of cancer has been increasing. Cancer is an important health problem. Early diagnosis and treatment of cancer can significantly increase the survival rate of cancer patients. With the rapid development of computer technology and medical technology, there have been some ways to intelligently classify a large amount of medical data, such as extracting the structured vocabulary in a single piece of medical records from medical record books, building a topic model of medical records, and The corresponding categories are obtained by training the medical case topics. Or use prior knowledge to train input samples, and then classify cancer types, which helps to reduce the labor intensity of medical staff. [0003] In the t...

AI Technical Summary

Problems solved by technology

[0003]In the traditional medical data classification method, most of the data classified and analyzed adopt the existing fixed data, the data source is relatively limited, and it is impossible to carry out the actual medical record information of the user. Classification analysis, and medical record information is mostly complicated and specific medical record analysis and record texts. Due to the particularity of medical texts, the deviation of vocabulary in medical record information will lead to complete semantic inconsistency, and the accuracy of text extraction is low. As a result, the accuracy of classification of medical data such as medical record information is low

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