Extraction of BI-RADS findings from breast ultrasound reports in Chinese using deep learning approaches.
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 report...
| Publicado en: | International Journal of Medical Informatics Vol. 119; pp. 17 - 22 |
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| Autores principales: | , , , , , , , , , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Elsevier B.V.
Nov2018
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=132491116&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 132491116 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 13865056 JR4 jtl: International Journal of Medical Informatics issn: 13865056 maglogo: N pubinfo: dt: Nov2018 vid: 119 pid: 467 pub: Elsevier B.V. place: New York, New York artinfo: ui: 132491116 132491116 NLM30342682 132491116 10.1016/j.ijmedinf.2018.08.009 NLM30342682 132491116 ppf: 17 ppct: 5 formats: tig: atl: Extraction of BI-RADS findings from breast ultrasound reports in Chinese using deep learning approaches. aug: au: Miao, Shumei Xu, Tingyu Wu, Yonghui Xie, Hui Wang, Jingqi Jing, Shenqi Zhang, Yaoyun Zhang, Xiaoliang Yang, Yinshuang Zhang, Xin Shan, Tao Wang, Li Xu, Hua Wang, Shui Liu, Yun affil: Department of Information, The First Affiliated Hospital of Nanjing Medical University & Jiangsu Province Hospital, Nanjing, Jiangsu, China sug: subj: Radiology Information Systems Algorithms Image Interpretation, Computer Assisted Methods Breast Neoplasms Ultrasonography Methods Female Human China Validation Studies Comparative Studies Evaluation Research Multicenter Studies Arthritis Impact Measurement Scales Barthel Index Psychological Tests Scales Female ab: 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. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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