Structural Application of Medical Image Report Based on Bi-CNNs-LSTM-CRF

Abstract

Medical image reporting is an important information carrier for medical staff to record medical activities for patients. It contains a large number of technical terms and medical knowledge. Extracting effective information from medical imaging reports can better serve clinical decision making and promote the development of the medical field. This paper focuses on the breast medical imaging report, analyzes the structural characteristics of the report, designs the medical record structured template, extracts the text features from the image report, and forms the structured data in the canonical form. In this paper, the machine learning model bidirectional CNNs-LSTM-CRF is used to extract the characteristic information of related lesions in the image report. The experimental data comes from an imaging inspection report provided by a medical institution to evaluate the effect of the structure by predicting the BI-RADS classification information. The data was provided by a medical institution, and the extracting result of the feature tagging is that the average accuracy is 95.71%, the average recall is 98.10%, and the average F1 values 97.29%.