Text mining for translational bioinformatics

NCUIR > College of Electrical Engineering & Computer Science > Department of Computer Science and information Engineering > journal & Dissertation > Item 987654321/107351

Please use this identifier to cite or link to this item: https://ir.lib.ncu.edu.tw/handle/987654321/107351

Title:	Text mining for translational bioinformatics
Authors:	蔡宗翰;Dai, Hong-Jie;Wei, Chih-Hsuan;Kao, Hung-Yu;Liu, Rey-Long;Tsai, Richard Tzong-Han;Lu, Zhiyong
Contributors:	資訊電機學院資訊工程學系
Keywords:	Bioinformatics;Cardiovascular disease;Cardiovascular Diseases;Computational Biology;Computer science;Coronary vessels;Data Mining;Electronic health records;Engineering;Genes;Humans;Hypotheses;Informatics;Medical research;Metadata;Methods;Molecular biology;Performance evaluation;Physiology;Studies;Technology application;Translational Medical Research
Date:	2015-01-01
Issue Date:	2026-04-23 14:09:51 (UTC+8)
Publisher:	Hindawi Publishing Corporation;United States: Hindawi Publishing Corporation
Abstract:	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
Appears in Collections:	[Department of Computer Science and information Engineering] journal & Dissertation

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