Mining vaccine adverse events mentions from social media using Twitter as a source (Preprint)

2021 ◽  
Author(s):  
Sedigheh Khademi Habibabadi ◽  
Pari Delir Haghighi ◽  
Frada Burstein ◽  
Jim Buttery
Keyword(s):  
Social Media ◽  
Adverse Event ◽  
Adverse Events ◽  
Language Processing ◽  
Vaccine Safety ◽  
Safety Issues ◽  
Reporting Systems ◽  
Degree Of Confidence ◽  
High Degree ◽  
Safety Signals

BACKGROUND Traditional monitoring for Adverse Events Following Immunisation (AEFI) relies on various established reporting systems, where there is inevitably a lag between an AEFI occurring and its potential reporting, and subsequent processing of reports. AEFI safety signal detection strives to detect AEFI as early as possible, ideally close to real-time. Monitoring social media data holds promise as a resource for this. OBJECTIVE 1) To investigate the utility of monitoring social media for gaining early insights into vaccine safety issues, by extracting vaccine adverse event mentions (VAEM) from Twitter using natural language processing (NLP) techniques. 2) To document the NLP processes used and identify the most effective of them for successively identifying tweets that contain VAEM, with a view to defining an approach that might be applicable to other similar social media surveillance tasks. METHODS A VAEM-Mine method was developed that combines topic modelling with classification techniques to extract maximal VAEM posts from a vaccine-related Twitter stream, with a high degree of confidence. The approach does not require a targeted search for specific vaccine reactions, but instead identifies any VAEM post within many unrelated posts. RESULTS The VAEM-Mine method successively isolates vaccine adverse event mentions from the massive amount of other vaccine-related Twitter posts, achieving an F1-Score of 0.91 in the classification phase. CONCLUSIONS Social media can assist with detection of vaccine safety signals as a valuable complementary source for monitoring mentions of vaccine adverse events. A social media based VAEM data stream can be assessed for changes to detect possible emerging vaccine safety signals, helping to address the well-recognised limitations of passive reporting systems, including timeliness and under-reporting.

scholarly journals Change Point Analysis for Detecting Vaccine Safety Signals

Vaccines ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 206
Author(s):  
Seung-Hun You ◽  
Eun Jin Jang ◽  
Myo-Song Kim ◽  
Min-Taek Lee ◽  
Ye-Jin Kang ◽  
...  
Keyword(s):  
Adverse Event ◽  
Change Point ◽  
Predictive Value ◽  
Public Safety ◽  
Vaccine Safety ◽  
Adverse Event Reporting ◽  
Change Point Analysis ◽  
Event Reporting ◽  
Point Analysis ◽  
Safety Signals

It is important to detect signals of abrupt changes in adverse event reporting in order to notice public safety concerns and take prompt action, especially for vaccines under national immunization programs. In this study, we assessed the applicability of change point analysis (CPA) for signal detection in vaccine safety surveillance. The performances of three CPA methods, namely Bayesian change point analysis, Taylor’s change point analysis (Taylor-CPA), and environmental time series change point detection (EnvCpt), were assessed via simulated data with assumptions for the baseline number of events and degrees of change. The analysis was validated using the Korea Adverse Event Reporting System (KAERS) database. In the simulation study, the Taylor-CPA method exhibited better results for the detection of a change point (accuracy of 96% to 100%, sensitivity of 7% to 100%, specificity of 98% to 100%, positive predictive value of 25% to 85%, negative predictive value of 96% to 100%, and balanced accuracy of 53% to 100%) than the other two CPA methods. When the CPA methods were applied to reports of syncope or dizziness following human papillomavirus (HPV) immunization in the KAERS database, Taylor-CPA and EnvCpt detected a change point (Q2/2013), which was consistent with actual public safety concerns. CPA can be applied as an efficient tool for the early detection of vaccine safety signals.

Adverse Event Prevention and Management

2016 ◽  
Author(s):  
Patrick J. Guffey ◽  
Martin Culwick
Keyword(s):  
Adverse Event ◽  
Quality Improvement ◽  
Adverse Events ◽  
Failure Mode ◽  
Healthcare System ◽  
Event Management ◽  
Incident Reporting Systems ◽  
Reporting Systems ◽  
Bow Tie

Adverse events are an unfortunate reality of caring for patients in our current healthcare system. Preventing and mitigating these events are an important part of quality improvement. First, an understanding of what events occur and how often they are occurring is critical to planning improvements. Incident reporting systems are one way of gathering this information. Then, events should be categorized and analyzed for improvement. The failure mode and effects analysis (FMEA) and bow-tie diagram are two tools for this purpose. Once an event has occurred, consideration should be given to the caregivers as well as the patient when managing and resolving adverse events. Prevention requires strong analysis of events and recognition of both latent (system) and human causes. Interventions have different degrees of effectiveness, ranging from highly effective forcing functions, to marginally effective encouraging statements. There are four steps to event management: mitigation, immediate management, refractory management, and follow-up.

Can Spontaneous Adverse Event Reporting Systems Really Be Used to Compare Rates of Adverse Events Between Drugs?

2007 ◽  
Vol 104 (2) ◽  
pp. 471-472 ◽  
Author(s):  
Paul M. Mertes ◽  
Anne B. Guttormsen ◽  
Torkel Harboe ◽  
S Gunnar O. Johansson ◽  
Erik Florvaag ◽  
...  
Keyword(s):  
Adverse Event ◽  
Adverse Events ◽  
Adverse Event Reporting ◽  
Event Reporting ◽  
Reporting Systems ◽  
Spontaneous Adverse Event

scholarly journals Vaccine safety in the next decade: why we need new modes of trust building

2021 ◽  
Vol 6 (Suppl 2) ◽  
pp. e003908
Author(s):  
Heidi J. Larson ◽  
Isabelle Sahinovic ◽  
Madhava Ram Balakrishnan ◽  
Clarissa Simas
Keyword(s):  
Social Media ◽  
New Technologies ◽  
Vaccine Safety ◽  
Safety Net ◽  
Global Network ◽  
Public Confidence ◽  
Media Campaigns ◽  
Safety Issues ◽  
Safety Concerns ◽  
The World

Among the realm of highly varied vaccine perceptions and concerns expressed by publics around the world, vaccine safety is the most frequently cited. While many of the safety questions raised have substantial evidence to address the concerns, vaccines do have small risks, and need vigilant and responsive systems to address them. With more and more new vaccines, combinations of vaccines and new technologies to develop and deliver them, new safety concerns will arise that need attention. Adding to this landscape is the dramatic impact which digital communication has had on how fast rumours and vaccine concerns can spread, making the task of the public health and scientific community even more pressing. One of the more recently characterised vaccine safety issues, now named ‘immunisation stress-related-response,’ has gained particularly high visibility given these highly globally connected social media networks. To better anticipate and address these rapidly shared vaccine safety concerns, a number of global efforts and local responses are being made. Co-created social media campaigns engaging parents and adolescents have been effective, while the WHO’s Vaccine Safety Net (VSN) initiative has grown its global network to increase awareness about vaccines and contribute to building confidence in vaccines. The VSN reviews websites around the world to assess their quality and accuracy to ensure and promote access to trustworthy and science-based information on vaccine safety for internet users. These and the efforts of the multiple network partners are more crucial than ever to sustain public confidence in this evolving vaccine safety landscape.

scholarly journals A Survey of the Impact of Patient Adverse Events and near Misses on Anaesthetists in Australia and New Zealand

2018 ◽  
Vol 46 (5) ◽  
pp. 510-515 ◽  
Author(s):  
R. Harrison ◽  
H. Lee ◽  
A. Sharma
Keyword(s):  
Adverse Event ◽  
New Zealand ◽  
Adverse Events ◽  
Incident Reporting ◽  
Near Miss ◽  
Near Misses ◽  
Incident Reporting Systems ◽  
Safety Incident ◽  
Reporting Systems ◽  
The Impact

We conducted a cross-sectional online survey of members of the Australian and New Zealand College of Anaesthetists to investigate their experiences of adverse patient safety events and near misses, including their use of incident reporting systems and the organisational support available. There were 247 respondents. Of the 243 anaesthetists whose patients had an adverse event or near miss, 199 reported this had affected them personally or professionally; 177 reported stress, 153 anxiety, 109 sleep disturbance, and 127 lower professional confidence. Of 188 who had reported an adverse event using their local incident reporting systems, 68 were satisfied with this process, 136 received useful feedback, 114 saw local improvements, and 104 saw system changes. Two hundred and thirty-four reported feeling determined to improve, and 228 were anxious about the potential for future errors. Seventy-five anaesthetists admitted not reporting a safety incident that they knew they should have. Reasons for not reporting included an impression that nothing would improve from incident reporting, that reporting was onerous, or fears of punitive action. These findings should spur anaesthetists, anaesthetic departments and professional organisations across Australia and New Zealand to examine their support mechanisms in relation to adverse events and errors and their incident reporting mechanisms, and to attempt to improve these services where necessary.

scholarly journals Using a Machine Learning Approach to Monitor COVID-19 Vaccine Adverse Events (VAE) from Twitter Data

Vaccines ◽  
2022 ◽  
Vol 10 (1) ◽  
pp. 103
Author(s):  
Andrew T. Lian ◽  
Jingcheng Du ◽  
Lu Tang
Keyword(s):  
Machine Learning ◽  
New York ◽  
Social Media ◽  
Adverse Effects ◽  
Adverse Events ◽  
Language Processing ◽  
Twitter Data ◽  
Personal Experiences ◽  
Machine Learning Approach ◽  
The Us

Social media can be used to monitor the adverse effects of vaccines. The goal of this project is to develop a machine learning and natural language processing approach to identify COVID-19 vaccine adverse events (VAE) from Twitter data. Based on COVID-19 vaccine-related tweets (1 December 2020–1 August 2021), we built a machine learning-based pipeline to identify tweets containing personal experiences with COVID-19 vaccinations and to extract and normalize VAE-related entities, including dose(s); vaccine types (Pfizer, Moderna, and Johnson & Johnson); and symptom(s) from tweets. We further analyzed the extracted VAE data based on the location, time, and frequency. We found that the four most populous states (California, Texas, Florida, and New York) in the US witnessed the most VAE discussions on Twitter. The frequency of Twitter discussions of VAE coincided with the progress of the COVID-19 vaccinations. Sore to touch, fatigue, and headache are the three most common adverse effects of all three COVID-19 vaccines in the US. Our findings demonstrate the feasibility of using social media data to monitor VAEs. To the best of our knowledge, this is the first study to identify COVID-19 vaccine adverse event signals from social media. It can be an excellent supplement to the existing vaccine pharmacovigilance systems.

scholarly journals Identifying Herbal Adverse Events From Spontaneous Reporting Systems Using Taxonomic Name Resolution Approach

2020 ◽  
Vol 14 ◽  
pp. 117793222092135
Author(s):  
Vivekanand Sharma ◽  
Luiz Fernando Fracassi Gelin ◽  
Indra Neil Sarkar
Keyword(s):  
Adverse Events ◽  
Spontaneous Reporting ◽  
Cannabis Sativa ◽  
Adverse Event Reporting System ◽  
Specific Area ◽  
Adverse Event Reporting ◽  
Safety Issues ◽  
Supplement Use ◽  
Potential Safety ◽  
Reporting Systems

The efficacy and safety of herbal supplements suffer from challenges due to non-uniform representation of ingredient terms within biomedical and observational health data sources. The nature of how supplement data are reported within Spontaneous Reporting Systems (SRS) can limit analyses of supplement-associated adverse events due to the use of incorrect nomenclature or failing to identify herbs. This study aimed to extract, standardize, and summarize supplement-relevant reports from two SRSs: (1) Food and Drug Administration Adverse Event Reporting System (FAERS) and (2) Canada Vigilance Adverse Reaction (CVAR) database. A thesaurus of plant names was developed and integrated with a mapping and normalization approach that accommodated misspellings and variants. The reports gathered from FAERS between the years 2004 and 2016 show 185,915 herbal and 7,235,330 non-herbal accounting for 2.51%. The data from CVAR found 36,940 reports of herbal and 503,580 non-herbal reports between the years 1965 and 2017 for a total of 6.83%. Although not all cases were actual adverse events due to numerous variables and incomplete reporting, it is interesting to note that the herbs most frequently reported and significantly associated with adverse events were as follows: Avena sativa (Oats), Cannabis sativa (marijuana), Digitalis purpurea (foxglove), Humulus lupulus (hops), Hypericum perforatum (St John’s Wort), Paullinia cupana (guarana), Phleum pretense (timothy-grass), Silybum marianum (milk thistle), Taraxacum officinale (Dandelion), and Valeriana officinalis (valerian). Using a scalable approach for mapping and resolution of herb names allowed data-driven exploration of potential adverse events from sources that have remained isolated in this specific area of research. The results from this study highlight several herb-associated safety issues providing motivation for subsequent in-depth analyses, including those that focus on the scope and severity of potential safety issues with supplement use.

scholarly journals Assessment of the Potential Adverse Events Related to Ribavirin-Interferon Combination for Novel Coronavirus Therapy

2020 ◽  
Vol 2020 ◽  
pp. 1-7
Author(s):  
Wenya Shan ◽  
Dongsheng Hong ◽  
Jieqiang Zhu ◽  
Qingwei Zhao
Keyword(s):  
Adverse Event ◽  
Adverse Events ◽  
Adverse Event Reporting System ◽  
Adverse Event Reporting ◽  
Drug Event ◽  
Reporting Odds Ratio ◽  
Serious Adverse Events ◽  
Bayesian Confidence Interval ◽  
Novel Coronavirus ◽  
Safety Signals

Purpose. We aimed to analyze and evaluate the safety signals of ribavirin-interferon combination through data mining of the US Food and Drug Administration Adverse Event Reporting System (FAERS), so as to provide reference for the rationale use of these agents in the management of relevant toxicities emerging in patients with novel coronavirus pneumonia (COVID-19). Methods. Reports to the FAERS from 1 January 2004 to 8 March 2020 were analyzed. The proportion of report ratio (PRR), reporting odds ratio (ROR), and Bayesian confidence interval progressive neural network (BCPNN) method were used to detect the safety signals. Results. A total of 55 safety signals were detected from the top 250 adverse event reactions in 2200 reports, but 19 signals were not included in the drug labels. All the detected adverse event reactions were associated with 13 System Organ Classes (SOC), such as gastrointestinal, blood and lymph, hepatobiliary, endocrine, and various nervous systems. The most frequent adverse events were analyzed, and the results showed that females were more likely to suffer from anemia, vomiting, neutropenia, diarrhea, and insomnia. Conclusion. The ADE (adverse drug event) signal detection based on FAERS is helpful to clarify the potential adverse events related to ribavirin-interferon combination for novel coronavirus therapy; clinicians should pay attention to the adverse reactions of gastrointestinal and blood systems, closely monitor the fluctuations of the platelet count, and carry out necessary mental health interventions to avoid serious adverse events.

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