International Journal of Biomedical Science and Engineering
Volume 5, Issue 3, June 2017, Pages: 29-34
Received: Sep. 12, 2017;
Published: Sep. 14, 2017
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Li Yanhong, School of Information Management and Engineering, Shanghai University of Finance and Economics, Shanghai, China
Song Anmeng, School of Information Management and Engineering, Shanghai University of Finance and Economics, Shanghai, China
Wang Jingling, School of Information Management and Engineering, Shanghai University of Finance and Economics, Shanghai, China
This paper discusses the application of text mining in medical field at home and abroad based on overview and analysis of current literature data. Foreign researchers have specific text mining tools, and they use it in the search engine data and electronic medical record. In addition, it is also used to predict side effects between drugs. In China, text mining based on medical literature data occupies a large part. On the one hand, they can monitor the self disclosure of health information. On the other hand, they explore whether online information can help individuals get out of the disease. With the development of information technology, text mining will become more and more widely applied in the medical field in the future.
A Survey of Current Work in Medical Text Mining---Data Source Perspective, International Journal of Biomedical Science and Engineering.
Vol. 5, No. 3,
2017, pp. 29-34.
Luis Tari, Saadat Anwar, Shanshan Liang & James Cai1 etc. (2010) Discovering drug-drug interactions: A text-mining and reasoning approach based on properties of drug metabolism, bioinformatics Vol. 26, pages i547-i553.
Hodong Lee, Gwan-Su Yi & Jong (2008) E3Miner: a text mining tool for ubiquitin-proteinligases, Nucleic Acids Research, Vol. 36W416-W422.
Courtney, Diane, Armin& Karan (2010) Text and Structural Data Mining of Influenza Mentions in Web and Social Media Int. J. Environ. Res. Public Health, 7, 596-615.
Gesualdo, Stilo & Agricola (2013) Influenza-Like Illness Surveillance on Twitter through Automated Learning of Naïve Language, PLoS ONE., Vol. 8 Issue 12, p1-1.
Nikfarjam A, Sarker A & O’Connor K etc. (2015) Pharmacovigilance from social media: mining adverse drug reaction mentions using sequence labeling with word embedding cluster features, J Am Med Inform Assoc. 22(3): 671-81.
Abeed Sarker, Karen O’Connor & Rachel Ginn etc. (2016) Social Media Mining for Toxicovigilance: Automatic Monitoring of Prescription Medication Abuse from Twitter, Drug Saf. 39: 231–240.
Ginsberg, Mohebbi, Matthew & Patel etc. (2009) Detecting influenza epidemics using search engine query data, Nature 457, 1012-1014.
Philip, Yiling, David & Forrest (2008) Using Internet Searches for Influenza Surveillance, Healthcare Epidemilogy • CID 1443-1448.
Hulth, Rydevik & Linde (2009) Web Queries as a Source for Syndromic Surveillance, PLoS ONE., Vol. 4 Issue 2, p1-10.
Samaras, Garcia, Elena & Sicilia etc. (2012)Syndromic surveillance models using Web data: The case of scarlet fever in the UK, Informatics for Health & Social Care. Vol. 37 Issue 2, p106-124.
Luther, Mccart & Berndt etc. (2015) Improving Identification of Fall-Related Injuries in Ambulatory Care Using Statistical Text Mining, American Journal of Public Health, 105(6): 1168-73.
Hammond, Benari & Laundry etc.(2015) The Feasibility of Using Large-Scale Text Mining to Detect Adverse Childhood Experiences in a VA-Treated Population, Journal of Traumatic Stress, 28(6): 505.
Duggal, Shukla & Chandra etc. (2016) Predictive risk modeling for early hospital readmission of patients with diabetes in India, Internation Journal of Diabeted in Developing Countries, 1-10.
Casillas, Perez & Oronoz etc. (2016) Learning to extract adverse drug reaction events from electronic health records in Spanish, Expert Systems with Applications, 61: 235-245.
Ford, Carroll & Smith etc. (2016) Extracting information from the text of electronic medical records to improve case detection: A systematic review, J Am Med Inform Assoc, Vol. 5: 180.
He Dan, Jiang Miao, Zheng Chi etc. (2014) Exploring Relationship Among Symptom, Pattern and Medication Regularity of Hypertension Based on Text Mining Technology, Chinese Journal of Experimental Traditional Medical Formulea, Vol. 20, No. 19, pages 214-216.
Guo Hongtao, Zheng Guang, Zhao Jing etc. (2011) Traditional Chinese Medicine Therapeutic Characteristics in the Treatment of Influenza A Virus Subtype H1N1 Based on Data Mining, World Science and Technilogy/Modernization of Traditional Chinese Medicine and Materia Medica, Vol. 13, No. 5, pages 772-776.
Li Li, Zhou Qi, Zheng Guang etc. (2011) Based on Data Mining Techniques to Explore Medication Regularity of Chinese Patent Medicine and West Medicine Application for Chronic Gastritis, Chinese Journal of Experimental Traditional Medical Formulea, Vol. 17, No. 24, pages 228-231.
Guo Hongtao, Zheng Guang, Zhang Chi etc. (2010) Exploring Commonly Existed Networks of Chinese Herbal Medicines Used in Rheumatoid Arthritis and Diabetes Mellitus through Data Mining, World Science and Technilogy/Modernization of Traditional Chinese Medicine and Materia Medica, Vol. 12, No. 5, pages 818-822.
Wang Liying, Zheng Guang & Guo Hongtao etc. (2013) Regularity of clinical medication of hypertension analyzed with text mining approach, Institute of Basic Research In Clinical Medicine, China Academy of Chinese Medical Sciences, Vol. 28, No. 1, pages 60-63.
Ku, Chiu & Zhang etc. (2014) Journal of the Association for Information Science and Technology, 65(5): 928–947.
Lu Yanand Yong Tan (2014) Feeling Blue Go Online: An Empirical Study of Social Support Among Patients, Information Systems Research, Vol. 25, No. 4, pp. 690–709.