Extraction of BI-RADS findings from breast ultrasound reports in Chinese using deep learning approaches
| Autor: | Yun Liu, Shumei Miao, Xin Zhang, Li Wang, Yonghui Wu, Xiaoliang Zhang, Jingqi Wang, Tao Shan, Yinshuang Yang, Tingyu Xu, Yaoyun Zhang, Hua Xu, Shui Wang, Shenqi Jing, Hui Xie |
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| Rok vydání: | 2017 |
| Předmět: |
Conditional random field
medicine.medical_specialty China 020205 medical informatics Breast imaging Computer science Health Informatics BI-RADS Breast Neoplasms 02 engineering and technology computer.software_genre Machine Learning 03 medical and health sciences 0302 clinical medicine Breast cancer Deep Learning Named-entity recognition Electronic health record Image Interpretation Computer-Assisted 0202 electrical engineering electronic engineering information engineering medicine Humans Medical physics 030212 general & internal medicine Breast ultrasound medicine.diagnostic_test business.industry Deep learning medicine.disease Radiology Information Systems Female Artificial intelligence Ultrasonography Mammary business computer Algorithms |
| Zdroj: | International journal of medical informatics. 119 |
| ISSN: | 1872-8243 |
| Popis: | Background The wide adoption of electronic health record systems (EHRs) in hospitals in China has made large amounts of data available for clinical research including breast cancer. Unfortunately, much of detailed clinical information is embedded in clinical narratives e.g., breast radiology reports. The American College of Radiology (ACR) has developed a Breast Imaging Reporting and Data System (BI-RADS) to standardize the clinical findings from breast radiology reports. Objectives This study aims to develop natural language processing (NLP) methods to extract BI-RADS findings from breast ultrasound reports in Chinese, thus to support clinical operation and breast cancer research in China. Methods We developed and compared three different types of NLP approaches, including a rule-based method, a traditional machine learning-based method using the Conditional Random Fields (CRF) algorithm, and deep learning-based approaches, to extract all BI-RADS finding categories from breast ultrasound reports in Chinese. Results Using a manually annotated dataset containing 540 reports, our evaluation shows that the deep learning-based method achieved the best F1-score of 0.904, when compared with rule-based and CRF-based approaches (0.848 and 0.881 respectively). Conclusions This is the first study that applies deep learning technologies to BI-RADS findings extraction in Chinese breast ultrasound reports, demonstrating its potential on enabling international collaborations on breast cancer research. |
| Databáze: | OpenAIRE |
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