Two quality control algorithms for echocardiographic report text based on natural language processing

Effective quality control of ultrasound examination report provides guarantee for the accuracy of clinical ultrasound diagnosis. Two quality control methods of ultrasound diagnosis report text are presented in this paper to detect abnormal words and evaluate completeness of report text, respectively. One is the detection of abnormal words in report text based on deep learning model of Chinese word segmentation (CWS), which realizes the detection of various abnormal words in the report text, such as misspelled words, unwanted repeated words and non-professional words. In this paper, a BILSTMCRF model combining character similarity features is proposed. The character similarity matrix is calculated by cosine similarity, and then the matrix is connected with the character encoding direction as the input data of the BiLSTM input layer. The other is Chinese sentence segment (CSS) in the ultrasonic report, mainly to cluster the clauses according to tissue and function of the heart. The experiment results show: (1) The improved deep learning model in this paper can accurately segment the text of echocardiography reports. The model with the most comprehensive effect on the verification set was selected as the final model of this paper based on the models trained with multiple sets of parameters. The accuracy rate of this model was 0.945, the recall rate was 0.939, and the F1 value was 0.947, all at a relatively high level. Can effectively detect abnormal words in the report. (2) The segmentation algorithm in this paper was used to test the results of automatic segmentation of echocardiographic reports combined with the results of manual segmentation by doctors. The accuracy of the dynamic segmentation method is above 90%, which can effectively segment the original echocardiogram report text according to the structure and function of the heart, and then be used to evaluate the completeness of the report description.