PMID- 29925405
OWN - NLM
STAT- MEDLINE
DCOM- 20190731
LR  - 20190731
IS  - 2041-1480 (Electronic)
VI  - 9
IP  - 1
DP  - 2018 Jun 20
TI  - Adverse event detection by integrating twitter data and VAERS.
PG  - 19
LID - 10.1186/s13326-018-0184-y [doi]
LID - 19
AB  - BACKGROUND: Vaccine has been one of the most successful public health 
      interventions to date. However, vaccines are pharmaceutical products that carry 
      risks so that many adverse events (AEs) are reported after receiving vaccines. 
      Traditional adverse event reporting systems suffer from several crucial 
      challenges including poor timeliness. This motivates increasing social 
      media-based detection systems, which demonstrate successful capability to capture 
      timely and prevalent disease information. Despite these advantages, social 
      media-based AE detection suffers from serious challenges such as labor-intensive 
      labeling and class imbalance of the training data. RESULTS: To tackle both 
      challenges from traditional reporting systems and social media, we exploit their 
      complementary strength and develop a combinatorial classification approach by 
      integrating Twitter data and the Vaccine Adverse Event Reporting System (VAERS) 
      information aiming to identify potential AEs after influenza vaccine. 
      Specifically, we combine formal reports which have accurately predefined labels 
      with social media data to reduce the cost of manual labeling; in order to combat 
      the class imbalance problem, a max-rule based multi-instance learning method is 
      proposed to bias positive users. Various experiments were conducted to validate 
      our model compared with other baselines. We observed that (1) multi-instance 
      learning methods outperformed baselines when only Twitter data were used; (2) 
      formal reports helped improve the performance metrics of our multi-instance 
      learning methods consistently while affecting the performance of other baselines 
      negatively; (3) the effect of formal reports was more obvious when the training 
      size was smaller. Case studies show that our model labeled users and tweets 
      accurately. CONCLUSIONS: We have developed a framework to detect vaccine AEs by 
      combining formal reports with social media data. We demonstrate the power of 
      formal reports on the performance improvement of AE detection when the amount of 
      social media data was small. Various experiments and case studies show the 
      effectiveness of our model.
FAU - Wang, Junxiang
AU  - Wang J
AD  - Department of Information Science and Technology, George Mason University, 
      Fairfax, VA, USA.
FAU - Zhao, Liang
AU  - Zhao L
AD  - Department of Information Science and Technology, George Mason University, 
      Fairfax, VA, USA.
FAU - Ye, Yanfang
AU  - Ye Y
AD  - Lane Department of Computer Science and Electrical Engineering, West Virginia 
      University, Morgantown, WV, USA.
AD  - Benjamin M. Statler College of Engineering and Mineral Resources, West Virginia 
      University, Morgantown, WV, USA.
FAU - Zhang, Yuji
AU  - Zhang Y
AUID- ORCID: 0000-0002-5429-6762
AD  - Department of Epidemiology & Public Health, University of Maryland School of 
      Medicine, Baltimore, MD, USA. Yuzhang@som.umaryland.edu.
AD  - Division of Biostatistics and Bioinformatics, University of Maryland Marlene and 
      Stewart Greenebaum Comprehensive Cancer Center, Baltimore, MD, USA. 
      Yuzhang@som.umaryland.edu.
LA  - eng
GR  - P30 CA134274/CA/NCI NIH HHS/United States
PT  - Journal Article
PT  - Research Support, N.I.H., Extramural
DEP - 20180620
PL  - England
TA  - J Biomed Semantics
JT  - Journal of biomedical semantics
JID - 101531992
RN  - 0 (Vaccines)
MH  - *Adverse Drug Reaction Reporting Systems
MH  - Data Mining
MH  - *Social Media
MH  - Vaccines/*adverse effects
PMC - PMC6011255
OTO - NOTNLM
OT  - Formal reports
OT  - Multi-instance learning
OT  - Social media
OT  - Vaccine adverse event detection
COIS- ETHICS APPROVAL AND CONSENT TO PARTICIPATE: Not applicable. COMPETING INTERESTS: 
      The authors declare that they have no competing interests. PUBLISHER'S NOTE: 
      Springer Nature remains neutral with regard to jurisdictional claims in published 
      maps and institutional affiliations.
EDAT- 2018/06/22 06:00
MHDA- 2019/08/01 06:00
PMCR- 2018/06/20
CRDT- 2018/06/22 06:00
PHST- 2018/02/02 00:00 [received]
PHST- 2018/05/10 00:00 [accepted]
PHST- 2018/06/22 06:00 [entrez]
PHST- 2018/06/22 06:00 [pubmed]
PHST- 2019/08/01 06:00 [medline]
PHST- 2018/06/20 00:00 [pmc-release]
AID - 10.1186/s13326-018-0184-y [pii]
AID - 184 [pii]
AID - 10.1186/s13326-018-0184-y [doi]
PST - epublish
SO  - J Biomed Semantics. 2018 Jun 20;9(1):19. doi: 10.1186/s13326-018-0184-y.