Analyzing Structures of Medical Imaging Diagnosis Reports

[Objective] This paper tries to turn medical imaging diagnosis reports into structured data, aiming to effectively extract information from these free-text-reports. [Methods] First, we analyzed the text characteristics of medical imaging diagnosis reports, and proposed a structuring method based on entity recognition and rule extraction. Then, we annotated 800 reports to construct datasets for model evaluation. [Results] The proposed method had a precision rate of 0.87 for all entities from the medical imaging diagnostic reports, which was 4.03% higher than that of the BERT-BiLSTM-CRF. Its recall rate was also 2.81% higher than that of the BERT-BiLSTM-CRF. Compared with the method of dependency analysis, the proposed model improved the recognition precision of medical exam items and results by 5.62% and 2.31%. [Limitations] We only examined the proposed method with diagnostic PET-CT imaging reports from one hospital. [Conclusions] This study successfully converts the free texts of medical imaging diagnostic reports to structured data. It not only optimizes the classification, storage, and retrieval of medical reports, but also provides supports for future research on medical imaging.