Ultrasonic examination follow-up patient screening method based on machine learning

Abstract

The invention provides an ultrasonic examination follow-up patient screening method based on machine learning. Due to the rapid development of the deep learning technology, the utilization of the natural language processing technology and the deep learning technology becomes an important means for analyzing medical texts, and the means is an effective way for replacing manual text screening. According to the method, word segmentation is carried out on text contents through a JIEBA word segmentation tool, a TF-IDF method and a Word2Vec algorithm are adopted to construct word vectors respectively, and a chi-square test method is further utilized to select feature vectors; and a classification model selects XGBoost, Lightgbm and CNN to perform training modeling on feature data, so automatic screening of an ultrasonic examination follow-up list is realized.

Description

technical field [0001] The invention relates to a screening method for ultrasound follow-up patients based on electronic health records, and belongs to the field of knowledge discovery of ultrasound follow-up. Background technique [0002] In recent years, with the rapid development of ultrasound technology, the application of ultrasound technology in clinical diagnosis has become more and more extensive, and it has become one of the standard configurations in most hospitals. Ultrasound examination will not expose patients to ionizing radiation and protect them from Radiation-induced cancer risk interference. The accuracy of ultrasound diagnosis depends on two aspects: first, whether the doctor can acquire clear enough images to support clinical diagnosis through the ultrasound probe; second, whether the ultrasound doctor gives a correct diagnosis description. Because the accuracy of diagnostic results affects the diagnosis and treatment of diseases, medical institutions in...

AI Technical Summary

Problems solved by technology

[0006] The technical problem to be solved by the present invention is: the traditional follow-up patient screening method has a large workload and low efficiency, and the accuracy often depends on the ability level of the staff and the cycle of filing medical history

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