Journal of Peking University (Health Sciences) ›› 2021, Vol. 53 ›› Issue (3): 623-627. doi: 10.19723/j.issn.1671-167X.2021.03.031
Previous Articles Next Articles
YANG Yu1,WANG Sheng-feng2,ZHAN Si-yan2,Δ()
CLC Number:
[1] | 崔燕宁. 药物安全与药物警戒[M]. 北京: 人民卫生出版社, 2014: 1-2. |
[2] |
Lester J, Neyarapally GA, Lipowski E, et al. Evaluation of FDA safety-related drug label changes in 2010[J]. Pharmacoepidemiol Drug Saf, 2013,22(3):302-305.
doi: 10.1002/pds.3395 pmid: 23280652 |
[3] |
Al DR, Stacey D, Kohen D, et al. Factors affecting patient reporting of adverse drug reactions: a systematic review[J]. Br J Clin Pharmacol, 2017,83(4):875-883.
doi: 10.1111/bcp.v83.4 |
[4] | 丁呈怡, 詹思延. 药品使用者自发报告的价值与促进策略[J]. 药物流行病学杂志, 2016,25(1):55-58. |
[5] | 赵云泽, 张竞文, 谢文静, 等. “社会化媒体”还是“社交媒体”?一组至关重要的概念的翻译和辨析[J]. 新闻记者, 2015(6):63-66. |
[6] |
Ghosh R, Lewis D. Aims and approaches of Web-RADR: a consortium ensuring reliable ADR reporting via mobile devices and new insights from social media[J]. Expert Opin Drug Saf, 2015,14(12):1845-1853.
doi: 10.1517/14740338.2015.1096342 |
[7] | Digital use around the world in July 2020: we are social[EB/OL]. (2020-09-06)[2020-11-20]. https://wearesocial.com/blog/2020/07/digital-use-around-the-world-in-july-2020#. |
[8] |
TenBarge AM, Riggins JL. Responding to unsolicited medical requests from health care professionals on pharmaceutical industry-owned social media sites: three pilot studies[J]. J Med Internet Res, 2018,20(10):e285.
doi: 10.2196/jmir.9643 |
[9] |
van Stekelenborg J, Ellenius J, Maskell S, et al. Recommendations for the use of social media in pharmacovigilance: lessons from IMI WEB-RADR[J]. Drug Saf, 2019,42(12):1393-1407.
doi: 10.1007/s40264-019-00858-7 pmid: 31446567 |
[10] |
Pierce C E, Bouri K, Pamer C, et al. Evaluation of Facebook and Twitter monitoring to detect safety signals for medical products: an analysis of recent FDA safety alerts[J]. Drug Saf, 2017,40(4):317-331.
doi: 10.1007/s40264-016-0491-0 |
[11] |
Sarker A, Ginn R, Nikfarjam A, et al. Utilizing social media data for pharmacovigilance: a review[J]. J Biomed Inform, 2015,54:202-212.
doi: 10.1016/j.jbi.2015.02.004 pmid: 25720841 |
[12] | Correia R, Li L, Rocha L. Monitoring potential drug interactions and reactions via network analysis of Instagram user timelines[J]. Pac Symp Biocomput, 2016,21:492-503. |
[13] |
Nikfarjam A, Ransohoff JD, Callahan A, et al. Early detection of adverse drug reactions in social health networks: a natural language processing pipeline for signal detection[J]. JMIR Public Health Surveill, 2019,5(2):e11264.
doi: 10.2196/11264 |
[14] |
Karapetiantz P, Bellet F, Audeh B, et al. Descriptions of adverse drug reactions are less informative in forums than in the French pharmacovigilance database but provide more unexpected reactions[J]. Front Pharmacol, 2018,9:439.
doi: 10.3389/fphar.2018.00439 |
[15] |
Karapetiantz P, Audeh B, Lillo-Le LA, et al. Signal detection for baclofen in web forums: a preliminary study[J]. Stud Health Technol Inform, 2018,247:421-425.
pmid: 29677995 |
[16] |
Kurzinger ML, Schuck S, Texier N, et al. Web-based signal detection using medical forums data in France: comparative analysis[J]. J Med Internet Res, 2018,20(11):e10466.
doi: 10.2196/10466 |
[17] | Leaman R, Wojtulewicz L, Sullivan R, et al. Towards inter-netage pharmacovigilance: extracting adverse drug reactions from user posts to health-related social networks[C]. Uppsala, Sweden: Association for Computational Linguistics, 2010. |
[18] |
Tricco AC, Zarin W, Lillie E, et al. Utility of social media and crowd-intelligence data for pharmacovigilance: a scoping review[J]. BMC Med Inform Decis Mak, 2018,18(1):38.
doi: 10.1186/s12911-018-0621-y |
[19] |
Powell GE, Seifert HA, Reblin T, et al. Social media listening for routine post-marketing safety surveillance[J]. Drug Saf, 2016,39(5):443-454.
doi: 10.1007/s40264-015-0385-6 |
[20] | Freifeld CC. Digital pharmacovigilance: the medwatcher system for monitoring adverse events through automated processing of internet social media and crowdsourcing[D]. Boston: Boston University, 2014. |
[21] |
Blumenthal KG, Topaz M, Zhou L, et al. Mining social media data to assess the risk of skin and soft tissue infections from allergen immunotherapy[J]. J Allergy Clin Immunol, 2019,144(1):129-134.
doi: 10.1016/j.jaci.2019.01.029 |
[22] | Duval FV, Silva F. Mining in Twitter for adverse events from malaria drugs: the case of doxycycline[J]. Cad Saude Publica, 2019,35(5):e33417. |
[23] |
Convertino I, Ferraro S, Blandizzi C, et al. The usefulness of listening social media for pharmacovigilance purposes: a systematic review[J]. Expert Opin Drug Saf, 2018,17(11):1081-1093.
doi: 10.1080/14740338.2018.1531847 pmid: 30285501 |
[24] |
Lardon J, Bellet F, Aboukhamis R, et al. Evaluating Twitter as a complementary data source for pharmacovigilance[J]. Expert Opin Drug Saf, 2018,17(8):763-774.
doi: 10.1080/14740338.2018.1499724 |
[25] |
Li Y, Jimeno YA, Xiao C. Combining social media and FDA adverse event reporting system to detect adverse drug reactions[J]. Drug Saf, 2020,43(9):893-903.
doi: 10.1007/s40264-020-00943-2 |
[26] | Bousquet C, Dahamna B, Guillemin-Lanne S, et al. The adverse drug reactions from patient reports in social media project: five major challenges to overcome to operationalize analysis and efficiently support pharmacovigilance process[J]. JMIR Res Protoc, 2017,6(9):e179. |
[27] |
Hoang T, Liu J, Pratt N, et al. Authenticity and credibility aware detection of adverse drug events from social media[J]. Int J Med Inform, 2018,120:101-115.
doi: S1386-5056(18)30326-5 pmid: 30409335 |
[28] |
Sloane R, Osanlou O, Lewis D, et al. Social media and pharmacovigilance: a review of the opportunities and challenges[J]. Br J Clin Pharmacol, 2015,80(4):910-920.
doi: 10.1111/bcp.12717 |
[29] |
Audeh B, Bellet F, Beyens MN, et al. Use of social media for pharmacovigilance activities: key findings and recommendations from the Vigi4Med project[J]. Drug Saf, 2020,43(9):835-851.
doi: 10.1007/s40264-020-00951-2 |
[30] | 魏巍. 药物不良反应知识发现与利用模型研究[D]. 武汉大学, 2017. |
[31] |
Wong A, Plasek JM, Montecalvo SP, et al. Natural language processing and its implications for the future of medication safety: a narrative review of recent advances and challenges[J]. Pharmacotherapy, 2018,38(8):822-841.
doi: 10.1002/phar.2018.38.issue-8 |
[32] | Meng X, Ganoe CH, Sieberg RT, et al. Self-supervised contextual language representation of radiology reports to improve the identification of communication urgency[J]. AMIA Jt Summits Transl Sci Proc, 2020,2020:413-421. |
[33] |
Pariente A, Gregoire F, Fourrier-Reglat A, et al. Impact of safety alerts on measures of disproportionality in spontaneous reporting databases: the notoriety bias[J]. Drug Saf, 2007,30(10):891-898.
doi: 10.2165/00002018-200730100-00007 |
[34] |
Rees S, Mian S, Grabowski N. Using social media in safety signal management: is it reliable?[J]. Ther Adv Drug Saf, 2018,9(10):591-599.
doi: 10.1177/2042098618789596 |
[35] |
Golder S, Scantlebury A, Christmas H. Understanding public attitudes toward researchers using social media for detecting and monitoring adverse events data: multi methods study[J]. J Med Internet Res, 2019,21(8):e7081.
doi: 10.2196/jmir.7081 |
[1] | CHENG Yin-chu, PAN Yong-ping, ZHANG Yang,PAN Yu-ting, DING Cheng-yi, CAO Yu, ZHUO Lin, FANG Ren-fei, GAO Ai-yu, GUO Jing, LI Ai-jun, FU Qiang, MA Jun, ZHAN Si-yan. Investigation of the cognition and behavior on drug safety in Beijing middle school students [J]. Journal of Peking University(Health Sciences), 2017, 49(6): 1038-1043. |
|