762 related articles for article (PubMed ID: 25688695)
1. Filtering big data from social media--Building an early warning system for adverse drug reactions.
Yang M; Kiang M; Shang W
J Biomed Inform; 2015 Apr; 54():230-40. PubMed ID: 25688695
[TBL] [Abstract][Full Text] [Related]
2. Portable automatic text classification for adverse drug reaction detection via multi-corpus training.
Sarker A; Gonzalez G
J Biomed Inform; 2015 Feb; 53():196-207. PubMed ID: 25451103
[TBL] [Abstract][Full Text] [Related]
3. Classifying adverse drug reactions from imbalanced twitter data.
Dai HJ; Wang CK
Int J Med Inform; 2019 Sep; 129():122-132. PubMed ID: 31445246
[TBL] [Abstract][Full Text] [Related]
4. Utilizing social media data for pharmacovigilance: A review.
Sarker A; Ginn R; Nikfarjam A; O'Connor K; Smith K; Jayaraman S; Upadhaya T; Gonzalez G
J Biomed Inform; 2015 Apr; 54():202-12. PubMed ID: 25720841
[TBL] [Abstract][Full Text] [Related]
5. Exploring Spanish health social media for detecting drug effects.
Segura-Bedmar I; Martínez P; Revert R; Moreno-Schneider J
BMC Med Inform Decis Mak; 2015; 15 Suppl 2(Suppl 2):S6. PubMed ID: 26100267
[TBL] [Abstract][Full Text] [Related]
6. Pharmacovigilance from social media: mining adverse drug reaction mentions using sequence labeling with word embedding cluster features.
Nikfarjam A; Sarker A; O'Connor K; Ginn R; Gonzalez G
J Am Med Inform Assoc; 2015 May; 22(3):671-81. PubMed ID: 25755127
[TBL] [Abstract][Full Text] [Related]
7. Leveraging graph topology and semantic context for pharmacovigilance through twitter-streams.
Eshleman R; Singh R
BMC Bioinformatics; 2016 Oct; 17(Suppl 13):335. PubMed ID: 27766937
[TBL] [Abstract][Full Text] [Related]
8. Mining Adverse Drug Reactions in Social Media with Named Entity Recognition and Semantic Methods.
Chen X; Deldossi M; Aboukhamis R; Faviez C; Dahamna B; Karapetiantz P; Guenegou-Arnoux A; Girardeau Y; Guillemin-Lanne S; Lillo-Le-Louët A; Texier N; Burgun A; Katsahian S
Stud Health Technol Inform; 2017; 245():322-326. PubMed ID: 29295108
[TBL] [Abstract][Full Text] [Related]
9. Analysis of the effect of sentiment analysis on extracting adverse drug reactions from tweets and forum posts.
Korkontzelos I; Nikfarjam A; Shardlow M; Sarker A; Ananiadou S; Gonzalez GH
J Biomed Inform; 2016 Aug; 62():148-58. PubMed ID: 27363901
[TBL] [Abstract][Full Text] [Related]
10. Evaluation of Internet Social Networks using Net scoring Tool: A Case Study in Adverse Drug Reaction Mining.
Katsahian S; Simond Moreau E; Leprovost D; Lardon J; Bousquet C; Kerdelhué G; Abdellaoui R; Texier N; Burgun A; Boussadi A; Faviez C
Stud Health Technol Inform; 2015; 210():526-30. PubMed ID: 25991203
[TBL] [Abstract][Full Text] [Related]
11. An ensemble heterogeneous classification methodology for discovering health-related knowledge in social media messages.
Tuarob S; Tucker CS; Salathe M; Ram N
J Biomed Inform; 2014 Jun; 49():255-68. PubMed ID: 24642081
[TBL] [Abstract][Full Text] [Related]
12. SOCIAL MEDIA MINING SHARED TASK WORKSHOP.
Sarker A; Nikfarjam A; Gonzalez G
Pac Symp Biocomput; 2016; 21():581-92. PubMed ID: 26776221
[TBL] [Abstract][Full Text] [Related]
13. The Adverse Drug Reactions From Patient Reports in Social Media Project: Protocol for an Evaluation Against a Gold Standard.
Arnoux-Guenegou A; Girardeau Y; Chen X; Deldossi M; Aboukhamis R; Faviez C; Dahamna B; Karapetiantz P; Guillemin-Lanne S; Lillo-Le Louët A; Texier N; Burgun A; Katsahian S
JMIR Res Protoc; 2019 May; 8(5):e11448. PubMed ID: 31066711
[TBL] [Abstract][Full Text] [Related]
14. The Adverse Drug Reactions from Patient Reports in Social Media Project: Five Major Challenges to Overcome to Operationalize Analysis and Efficiently Support Pharmacovigilance Process.
Bousquet C; Dahamna B; Guillemin-Lanne S; Darmoni SJ; Faviez C; Huot C; Katsahian S; Leroux V; Pereira S; Richard C; Schück S; Souvignet J; Lillo-Le Louët A; Texier N
JMIR Res Protoc; 2017 Sep; 6(9):e179. PubMed ID: 28935617
[TBL] [Abstract][Full Text] [Related]
15. Descriptions of Adverse Drug Reactions Are Less Informative in Forums Than in the French Pharmacovigilance Database but Provide More Unexpected Reactions.
Karapetiantz P; Bellet F; Audeh B; Lardon J; Leprovost D; Aboukhamis R; Morlane-Hondère F; Grouin C; Burgun A; Katsahian S; Jaulent MC; Beyens MN; Lillo-Le Louët A; Bousquet C
Front Pharmacol; 2018; 9():439. PubMed ID: 29765326
[No Abstract] [Full Text] [Related]
16. Investigating patient narratives posted on Internet and their informativeness level for pharmacovigilance purpose: The example of comments about statins.
Kheloufi F; Default A; Blin O; Micallef J
Therapie; 2017 Sep; 72(4):483-490. PubMed ID: 28065444
[TBL] [Abstract][Full Text] [Related]
17. Mining heterogeneous networks with topological features constructed from patient-contributed content for pharmacovigilance.
Yang CC; Yang H
Artif Intell Med; 2018 Aug; 90():42-52. PubMed ID: 30093253
[TBL] [Abstract][Full Text] [Related]
18. Pharmacovigilance on twitter? Mining tweets for adverse drug reactions.
O'Connor K; Pimpalkhute P; Nikfarjam A; Ginn R; Smith KL; Gonzalez G
AMIA Annu Symp Proc; 2014; 2014():924-33. PubMed ID: 25954400
[TBL] [Abstract][Full Text] [Related]
19. Complementing the US Food and Drug Administration Adverse Event Reporting System With Adverse Drug Reaction Reporting From Social Media: Comparative Analysis.
Zhou Z; Hultgren KE
JMIR Public Health Surveill; 2020 Sep; 6(3):e19266. PubMed ID: 32996889
[TBL] [Abstract][Full Text] [Related]
20. 4-Fluoramphetamine in the Netherlands: Text-mining and sentiment analysis of internet forums.
Blankers M; van der Gouwe D; van Laar M
Int J Drug Policy; 2019 Feb; 64():34-39. PubMed ID: 30551004
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]