Identifying diagnosis evidence of cardiogenic stroke from Chinese echocardiograph reports

Abstract

Our machine learning method achieved the average performance on the diagnosis evidence identification is 98.03, 90.17 and 93.94% respectively. In addition, our method is capable to identify the novel diagnosis evidence of cardiogenic stroke description such as "-" (mitral stenosis), "" (aortic valve calcification) et al. CONCLUSIONS: In this study, we analyze the structure of the echocardiograph reports and summarized 149 phrases on diagnosis evidence of cardiogenic stroke. We use the phrases to generate an annotated corpus automatically, which greatly reduces the cost of manual annotation. The model trained based on the corpus also has a good performance on the testing set. The method of automatically identifying diagnosis evidence of cardiogenic stroke proposed in this study will be further refined in the practice.

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