Quality Management of Pulmonary Nodule Radiology Reports Based on Natural Language Processing
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...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-06-01
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| Series: | Bioengineering |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2306-5354/9/6/244 |
| _version_ | 1797489915117699072 |
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| author | Xiaolu Fei Pengyu Chen Lan Wei Yue Huang Yi Xin Jia Li |
| author_facet | Xiaolu Fei Pengyu Chen Lan Wei Yue Huang Yi Xin Jia Li |
| author_sort | Xiaolu Fei |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T00:23:27Z |
| format | Article |
| id | doaj.art-1c402bb41af4465897686b7637f93dc7 |
| institution | Directory Open Access Journal |
| issn | 2306-5354 |
| language | English |
| last_indexed | 2024-03-10T00:23:27Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Bioengineering |
| spelling | doaj.art-1c402bb41af4465897686b7637f93dc72023-11-23T15:37:58ZengMDPI AGBioengineering2306-53542022-06-019624410.3390/bioengineering9060244Quality Management of Pulmonary Nodule Radiology Reports Based on Natural Language ProcessingXiaolu Fei0Pengyu Chen1Lan Wei2Yue Huang3Yi Xin4Jia Li5Information Center, Xuanwu Hospital, Capital Medical University, Beijing 100053, ChinaInformation Center, Xuanwu Hospital, Capital Medical University, Beijing 100053, ChinaInformation Center, Xuanwu Hospital, Capital Medical University, Beijing 100053, ChinaInformation Center, Xuanwu Hospital, Capital Medical University, Beijing 100053, ChinaSchool of Life Science, Beijing Institute of Technology, Beijing 100081,ChinaInformation Center, Xuanwu Hospital, Capital Medical University, Beijing 100053, ChinaTo 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.https://www.mdpi.com/2306-5354/9/6/244natural language processingradiology reportquality managementknowledge graphpulmonary nodule |
| spellingShingle | Xiaolu Fei Pengyu Chen Lan Wei Yue Huang Yi Xin Jia Li Quality Management of Pulmonary Nodule Radiology Reports Based on Natural Language Processing Bioengineering natural language processing radiology report quality management knowledge graph pulmonary nodule |
| title | Quality Management of Pulmonary Nodule Radiology Reports Based on Natural Language Processing |
| title_full | Quality Management of Pulmonary Nodule Radiology Reports Based on Natural Language Processing |
| title_fullStr | Quality Management of Pulmonary Nodule Radiology Reports Based on Natural Language Processing |
| title_full_unstemmed | Quality Management of Pulmonary Nodule Radiology Reports Based on Natural Language Processing |
| title_short | Quality Management of Pulmonary Nodule Radiology Reports Based on Natural Language Processing |
| title_sort | quality management of pulmonary nodule radiology reports based on natural language processing |
| topic | natural language processing radiology report quality management knowledge graph pulmonary nodule |
| url | https://www.mdpi.com/2306-5354/9/6/244 |
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