Fei Xiaolu, Chen Pengyu, Wei Lan, Huang Yue, Xin Yi, Li Jia
Information Center, Xuanwu Hospital, Capital Medical University, Beijing 100053, China.
School of Life Science, Beijing Institute of Technology, Beijing 100081,China.
Bioengineering (Basel). 2022 Jun 1;9(6):244. doi: 10.3390/bioengineering9060244.
To investigate the feasibility of automated follow-up recommendations based on findings in radiology reports, this paper proposed a Natural Language Processing model specific for Pulmonary Nodule Radiology Reports. Unstructured findings used to describe pulmonary nodules in 48,091 radiology reports were processed in this study. We established an NLP model to extract information entities from findings of radiology reports, using deep learning and conditional random-field algorithms. Subsequently, we constructed a knowledge graph comprising 168 entities and four relationships, based on the export recommendations of the internationally renowned Fleischner Society for pulmonary nodules. These were employed in combination with rule templates to automatically generate follow-up recommendations. The automatically generated recommendations were then compared to the impression part of the reports to evaluate the matching rate of proper follow ups in the current situation. The NLP model identified eight types of entities with a recognition accuracy of up to 94.22%. A total of 43,898 out of 48,091 clinical reports were judged to contain appropriate follow-up recommendations, corresponding to the matching rate of 91.28%. The results show that NLP can be used on Chinese radiology reports to extract structured information at the content level, thereby realizing the prompt and intelligent follow-up suggestion generation or post-quality management of follow-up recommendations.
为研究基于放射学报告结果进行自动随访建议的可行性，本文提出了一种针对肺结节放射学报告的自然语言处理模型。本研究对48091份放射学报告中用于描述肺结节的非结构化结果进行了处理。我们建立了一个自然语言处理模型，使用深度学习和条件随机场算法从放射学报告结果中提取信息实体。随后，我们根据国际知名的弗莱施纳学会关于肺结节的出口建议，构建了一个包含168个实体和四种关系的知识图谱。这些与规则模板结合使用，以自动生成随访建议。然后将自动生成的建议与报告的印象部分进行比较，以评估当前情况下适当随访的匹配率。该自然语言处理模型识别出八种类型的实体，识别准确率高达94.22%。48091份临床报告中共有43898份被判定包含适当的随访建议，匹配率为91.28%。结果表明，自然语言处理可用于中文放射学报告，在内容层面提取结构化信息，从而实现随访建议的快速智能生成或随访建议的事后质量管理。
