Social media and pharmacovigilance: A review of the opportunities and challenges.
暂无分享,去创建一个
Danushka Bollegala | Munir Pirmohamed | Orod Osanlou | Simon Maskell | M. Pirmohamed | Danushka Bollegala | R. Sloane | S. Maskell | D. Lewis | O. Osanlou | Richard Sloane | David Lewis
[1] S. Labott,et al. Using Social Media in Research: New Ethics for a New Meme? , 2014, The American journal of bioethics : AJOB.
[2] M. Hauben,et al. Quantitative Methods in Pharmacovigilance , 2003, Drug safety.
[3] Hsinchun Chen,et al. AZDrugMiner: An Information Extraction System for Mining Patient-Reported Adverse Drug Events in Online Patient Forums , 2013, ICSH.
[4] Ming Yang,et al. Filtering big data from social media - Building an early warning system for adverse drug reactions , 2015, J. Biomed. Informatics.
[5] Graciela Gonzalez-Hernandez,et al. Utilizing social media data for pharmacovigilance: A review , 2015, J. Biomed. Informatics.
[6] Danushka Bollegala,et al. A Cross-Lingual Similarity Measure for Detecting Biomedical Term Translations , 2015, PloS one.
[7] Marie Lindquist,et al. Social Media and Networks in Pharmacovigilance , 2011, Drug safety.
[8] Viroj Wiwanitkit,et al. Using social media. , 2014, Journal of the American Dental Association.
[9] Taha A. Kass-Hout,et al. Digital Drug Safety Surveillance: Monitoring Pharmaceutical Products in Twitter , 2014, Drug Safety.
[10] Jian Yang,et al. Towards Internet-Age Pharmacovigilance: Extracting Adverse Drug Reactions from User Posts in Health-Related Social Networks , 2010, BioNLP@ACL.
[11] Jacob E Simmering,et al. Web search query volume as a measure of pharmaceutical utilization and changes in prescribing patterns. , 2014, Research in social & administrative pharmacy : RSAP.
[12] Bernhard Schölkopf,et al. Correcting Sample Selection Bias by Unlabeled Data , 2006, NIPS.
[13] Nazli Goharian,et al. ADRTrace: Detecting Expected and Unexpected Adverse Drug Reactions from User Reviews on Social Media Sites , 2013, ECIR.
[14] Hsinchun Chen,et al. Identifying Adverse Drug Events from Health Social Media: A Case Study on Heart Disease Discussion Forums , 2014, ICSH.
[15] Abeed Sarker,et al. Portable automatic text classification for adverse drug reaction detection via multi-corpus training , 2015, J. Biomed. Informatics.
[16] Yen S. Low,et al. Text Mining for Adverse Drug Events: the Promise, Challenges, and State of the Art , 2014, Drug Safety.
[17] V. C. Padaki,et al. Smart Vest: wearable multi-parameter remote physiological monitoring system. , 2008, Medical engineering & physics.
[18] Thomas Joseph,et al. A pipeline to extract drug-adverse event pairs from multiple data sources , 2014, BMC Medical Informatics and Decision Making.
[19] M. Field,et al. Ethical conduct of clinical research involving children. , 2004, Bulletin of medical ethics.
[20] James Spellos. Using social media. , 2013, Journal of continuing education in nursing.
[21] K. Sonawane,et al. Serious adverse Drug events reported to the Food and Drug Administration (FDA): analysis of the FDA adverse event reporting system (FAERS) 2006-2011 database , 2015 .
[22] William DuMouchel,et al. Empirical bayes model to combine signals of adverse drug reactions , 2013, KDD.
[24] Graciela Gonzalez,et al. Phonetic Spelling Filter for Keyword Selection in Drug Mention Mining from Social Media , 2014, AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science.
[25] Dong Nguyen,et al. "How Old Do You Think I Am?" A Study of Language and Age in Twitter , 2013, ICWSM.
[26] Birgit Kraft,et al. Opioid medication and driving ability , 2005, European journal of pain.
[27] Fan Yu,et al. Towards large-scale twitter mining for drug-related adverse events , 2012, SHB '12.
[28] Yutaka Matsuo,et al. Earthquake shakes Twitter users: real-time event detection by social sensors , 2010, WWW '10.
[29] Keyuan Jiang,et al. Mining Twitter Data for Potential Drug Effects , 2013, ADMA.
[30] Bo Luo,et al. Mining Adverse Drug Reactions from online healthcare forums using Hidden Markov Model , 2014, BMC Medical Informatics and Decision Making.
[31] Tsuhan Chen,et al. An active learning framework for content-based information retrieval , 2002, IEEE Trans. Multim..
[32] B. Wilfond,et al. Ethical Implications of Social Media in Health Care Research , 2014, The American journal of bioethics : AJOB.
[33] W. Inman,et al. Under-reporting of adverse drug reactions. , 1985, British medical journal.
[34] J. Brownstein,et al. Social and news media enable estimation of epidemiological patterns early in the 2010 Haitian cholera outbreak. , 2012, The American journal of tropical medicine and hygiene.
[35] Vasanth Kattalai Kailasam,et al. Can Social Media Help Mental Health Practitioners Prevent Suicides? Anecdotal Evidence Suggests That Analyzing Facebook Posts Can Lead to Earlier Intervention , 2015 .
[36] Danushka Bollegala,et al. Automatic Discovery of Personal Name Aliases from the Web , 2011, IEEE Transactions on Knowledge and Data Engineering.
[37] Abeed Sarker,et al. Pharmacovigilance from social media: mining adverse drug reaction mentions using sequence labeling with word embedding cluster features , 2015, J. Am. Medical Informatics Assoc..
[38] Azadeh Nikfarjam,et al. Pattern mining for extraction of mentions of Adverse Drug Reactions from user comments. , 2011, AMIA ... Annual Symposium proceedings. AMIA Symposium.
[39] Jelena Hadzi-Puric,et al. Automatic Drug Adverse Reaction Discovery from Parenting Websites Using Disproportionality Methods , 2012, 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining.
[40] D. Barnard-Wills. UK News Media Discourses of Surveillance , 2011 .
[41] Paloma Martínez,et al. Detecting drugs and adverse events from Spanish social media streams , 2014, Louhi@EACL.
[42] Janine A Clayton,et al. Enrolling pregnant women: issues in clinical research. , 2013, Women's health issues : official publication of the Jacobs Institute of Women's Health.
[43] Carol Friedman,et al. Combing signals from spontaneous reports and electronic health records for detection of adverse drug reactions , 2013, J. Am. Medical Informatics Assoc..
[44] Richard B. Berlin,et al. Predicting adverse drug events from personal health messages. , 2011, AMIA ... Annual Symposium proceedings. AMIA Symposium.
[45] Muhammad Aurangzeb Ahmad,et al. Trust, distrust and lack of confidence of users in online social media-sharing communities , 2013, Knowl. Based Syst..
[46] Graciela Gonzalez-Hernandez,et al. Pharmacovigilance on Twitter? Mining Tweets for Adverse Drug Reactions , 2014, AMIA.
[47] Nicholas Moore,et al. Biases affecting the proportional reporting ratio (PRR) in spontaneous reports pharmacovigilance databases: the example of sertindole , 2003, Pharmacoepidemiology and drug safety.
[48] Dong-Ling Xu,et al. Evidential reasoning rule for evidence combination , 2013, Artif. Intell..
[49] S. Janković,et al. Using Facebook to Increase Spontaneous Reporting of Adverse Drug Reactions , 2011, Drug safety.
[50] Christopher C. Yang,et al. Postmarketing Drug Safety Surveillance Using Publicly Available Health-Consumer-Contributed Content in Social Media , 2014, TMIS.
[51] Azadeh Nikfarjam,et al. Mining Twitter for Adverse Drug Reaction Mentions : A Corpus and Classification Benchmark , 2014 .
[52] Yang Hao,et al. Detecting Vital Signs with Wearable Wireless Sensors , 2010, Sensors.
[53] Christopher C. Yang,et al. Social media mining for drug safety signal detection , 2012, SHB '12.
[54] Amay J Bandodkar,et al. Non-invasive wearable electrochemical sensors: a review. , 2014, Trends in biotechnology.
[55] M. Pirmohamed,et al. Adverse drug reactions as cause of admission to hospital: prospective analysis of 18 820 patients , 2004, BMJ : British Medical Journal.
[56] Sune Lehmann,et al. Understanding the Demographics of Twitter Users , 2011, ICWSM.
[57] M. Dramé,et al. Notoriety bias in a database of spontaneous reports: the example of osteonecrosis of the jaw under bisphosphonate therapy in the French national pharmacovigilance database , 2014, Pharmacoepidemiology and drug safety.
[58] Lyle H. Ungar,et al. Identifying potential adverse effects using the web: A new approach to medical hypothesis generation , 2011, J. Biomed. Informatics.