| 摘要: | 摘要： Hong-Jie Dai 1 and Chih-Hsuan Wei 2 and Hung-Yu Kao 3 and Rey-Long Liu 4 and Richard Tzong-Han Tsai 5 and Zhiyong Lu 2 1, Department of Computer Science and Information Engineering, National Taitung University, Taitung City 950, Taiwan 2, National Center for Biotechnology Information, National Library of Medicine, 8600 Rockville Pike, Bethesda, MD 20894, USA 3, Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan 701, Taiwan 4, Department of Medical Informatics, Tzu Chi University, Hualien 970, Taiwan 5, Department of Computer Science and Information Engineering, National Central University, Taoyuan 320, Taiwan Received 22 July 2015; Accepted 22 July 2015; 25 August 2015 This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The authors employed a hybrid approach combining both rule-based and machine learning clinical text mining techniques and achieved an averaged overall micro [figure omitted; refer to PDF] -score of 0.8302 for identifying and tracking risk factors including coronary artery diseases, diabetes, hyperlipidemia, hypertension, medication, obesity, family illness, and smoking histories.
其他題名： Biomed Res Int
出版者： United States: Hindawi Publishing Corporation
出版日期： 2015-01-01
出處： BioMed research international, 2015-01, Vol.2015, p.1-2
資源來源： Publicly Available Content Database
版權： Copyright © 2015 Hong-Jie Dai et al.
版權： COPYRIGHT 2015 John Wiley & Sons, Inc.
版權： Copyright © 2015 Hong-Jie Dai et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
版權： Copyright © 2015 Hong-Jie Dai et al. 2015
識別號： ISSN: 2314-6133
識別號： ISSN: 2314-6141
識別號： EISSN: 2314-6141
識別號： DOI: 10.1155/2015/368264
識別號： PMID: 26380272 |
