scholarly journals Social Media Analytics for Pharmacovigilance of Antiepileptic Drugs

2022 ◽  
Vol 2022 ◽  
pp. 1-24
Author(s):  
Anwar Ali Yahya ◽  
Yousef Asiri ◽  
Ibrahim Alyami
Keyword(s):  
Social Media ◽  
Spontaneous Reporting ◽  
Data Sources ◽  
Social Media Analytics ◽  
Reporting Odds Ratio ◽  
Seizure Freedom ◽  
Online Health Communities ◽  
Reporting Systems ◽  
Data Source ◽  
Health Communities

Epilepsy is a common neurological disorder worldwide and antiepileptic drug (AED) therapy is the cornerstone of its treatment. It has a laudable aim of achieving seizure freedom with minimal, if any, adverse drug reactions (ADRs). Too often, AED treatment is a long-lasting journey, in which ADRs have a crucial role in its administration. Therefore, from a pharmacovigilance perspective, detecting the ADRs of AEDs is a task of utmost importance. Typically, this task is accomplished by analyzing relevant data from spontaneous reporting systems. Despite their wide adoption for pharmacovigilance activities, the passiveness and high underreporting ratio associated with spontaneous reporting systems have encouraged the consideration of other data sources such as electronic health databases and pharmaceutical databases. Social media is the most recent alternative data source with many promising potentials to overcome the shortcomings of traditional data sources. Although in the literature some attempts have investigated the validity and utility of social media for ADR detection of different groups of drugs, none of them was dedicated to the ADRs of AEDs. Hence, this paper presents a novel investigation of the validity and utility of social media as an alternative data source for the detection of AED ADRs. To this end, a dataset of consumer reviews from two online health communities has been collected. The dataset is preprocessed; the unigram, bigram, and trigram are generated; and the ADRs of each AED are extracted with the aid of consumer health vocabulary and ADR lexicon. Three widely used measures, namely, proportional reporting ratio, reporting odds ratio, and information component, are used to measure the association between each ADR and AED. The resulting list of signaled ADRs for each AED is validated against a widely used ADR database, called Side Effect Resource, in terms of the precision of ADR detection. The validation results indicate the validity of online health community data for the detection of AED ADRs. Furthermore, the lists of signaled AED ADRs are analyzed to answer questions related to the common ADRs of AEDs and the similarities between AEDs in terms of their signaled ADRs. The consistency of the drawn answers with the existing pharmaceutical knowledge suggests the utility of the data from online health communities for AED-related knowledge discovery tasks.

Social Media analytics for Pharmacovigilance of Anti-Epileptic Drugs (Preprint)

2021 ◽  
Author(s):  
Anwar ESMAIL
Keyword(s):  
Social Media ◽  
Proportional Reporting Ratio ◽  
Social Media Analytics ◽  
Seizure Freedom ◽  
Consumer Health ◽  
Online Health Communities ◽  
Data Mining Algorithms ◽  
Data Source ◽  
Health Communities ◽  
Mining Algorithms

UNSTRUCTURED Epilepsy is a common neurological disorder worldwide and Anti-Epileptic Drugs (AEDs) therapy is the cornerstone of its treatment. It has a laudable aim of achieving seizure freedom and minimal, if any, Adverse Drug Reactions (ADRs). Too often, AEDs treatment is a long-lasting journey, in which ADRs have a crucial role in its administration. Therefore, from pharmacovigilance perspective, the detection of the ADRs of AEDs is a task of utmost importance. Typically, it is accomplished by applying data mining algorithms to a relevant data from spontaneous reporting systems. Despite their wide adoption for pharmacovigilance, the passiveness and high under-reporting ratio associated with them have encouraged considering other data source such as electronic health databases and pharmaceutical databases. Social media is the most recent alternative data source with many promising potentials to overcome the shortcomings of the traditional ones. Although, in the literature, some attempts have investigated the validity and utility of social media for ADRs detection of different groups of drugs, none of them was dedicated to the ADRs of AEDs. Hence, this paper presents a novel investigation of the validity and utility of social media as an alternative data source for the ADRs detection of AEDs. To this end, a dataset of consumers' reviews from two online health communities have been collected. The dataset is preprocessed, the unigram, bigram, and trigram are generated, and the ADRs of each AED are extracted with the aid of consumer health vocabulary and ADRs lexicon. Three widely used measures, namely proportional reporting ratio, reporting odd ratio, and information component are used to measure the association between each ADR and AED. The results, lists of signaled ADRs for each AED, are validated against Side Effect Resource (SIDER), a widely used ADRs database, in terms of precision of the ADRs detection. The validation results, 73%-74%, indicate the validity of the online health communities for the detection of AEDs ADRs. Furthermore, the lists of signaled AEDs ADRs are analyzed to answer questions regarding the common ADRs for all AEDs and the mutual similarities between AEDs in terms of their signaled ADRs. The consistency of the drawn answers with the existing pharmaceutical knowledge suggests the utility of the online health communities' data for knowledge discovery tasks of AEDs.

“The most inspiring bikini photos you’ll see this summer”: A thematic analysis of mass audiences’ interpretations of ostomy selfies

2018 ◽  
Vol 20 (10) ◽  
pp. 3858-3878 ◽  
Author(s):  
Mark A Rademacher
Keyword(s):  
Social Media ◽  
Crohn’S Disease ◽  
Thematic Analysis ◽  
News Coverage ◽  
The Body ◽  
Online Health Communities ◽  
News Stories ◽  
Mass Audience ◽  
The Creation ◽  
Health Communities

How people with ostomies—a surgically created opening in the body that expels bodily wastes—use social media to challenge ostomy stigma represents a growing area of research, especially the creation, posting, and circulation of ostomy selfies within online health communities. This project contributes to this research by examining reactions by a mass audience to news stories about a viral ostomy selfie posted by ostomate Bethany Townsend to a Crohn’s disease Facebook page. By analyzing the user-generated comments associated with this news coverage, this study illuminates how ostomy selfies are interpreted outside the highly sympathetic audiences that populate online health communities. Analysis reveals positive and negative reactions, posted by ostomates and non-ostomates alike, coexist within the comments. Implications of the conflicting reactions to ostomies, in general, and ostomy selfies, in particular, are discussed with regard to the effort to destigmatize ostomies in society.

scholarly journals Understanding an Urban Park through Big Data

2019 ◽  
Vol 16 (20) ◽  
pp. 3816 ◽  
Author(s):  
Jisoo Sim ◽  
Patrick Miller
Keyword(s):  
Social Media ◽  
Big Data ◽  
Data Analytics ◽  
Planning Process ◽  
Big Data Analytics ◽  
Urban Park ◽  
Social Media Analytics ◽  
Forest Park ◽  
Planning And Design ◽  
Data Source

To meet the needs of park users, planners and designers must know what park users want to do and how they want the park to offer different activities. Big data may help planners and designers gain this knowledge. This study examines how big data collected in an urban park could be used to identify meaningful implications for planning and design. While big data have emerged as a new data source, big data have not become an accepted source of data due to a lack of understanding of big data analytics. By comparing a survey as a traditional data source with big data, this study identifies the strengths and weaknesses of using big data analytics in park planning and design. There are two research questions: (1) what activities do park users want; and (2) how satisfied are users with different activities. The Gyeongui Line Forest Park, which was built on an abandoned railway, was selected as the study site. A total of 177 responses were collected through the onsite survey, and 3703 tweets mentioning the park were collected from Twitter. Results from the survey show that ordinary activities such as walking and taking a rest in the park were the most common. These findings also support existing studies. The results from social media analytics found notable things such as positive tweets about how the railway was turned into a park, and negative tweets about diseases that may occur in the park. Therefore, a survey as traditional data and social media analytics as big data can be complementary methods for the design and planning process.

scholarly journals Quantifying the Dynamics of Migration after a Disaster: Impact of Hurricane Maria in Puerto Rico

2020 ◽  
Author(s):  
Rolando J. Acosta ◽  
Nishant Kishore ◽  
Rafael A. Irizarry ◽  
Caroline Buckee
Keyword(s):  
Social Media ◽  
Puerto Rico ◽  
Census Data ◽  
Air Travel ◽  
Data Sources ◽  
Urban Centers ◽  
Population Loss ◽  
Data Source ◽  
Post Disaster ◽  
Wide Population

AbstractPopulation displacement may occur after natural disasters, permanently altering the demographic composition of the affected regions. Measuring this displacement is vital for both optimal post-disaster resource allocation and calculation of measures of public health interest such as mortality estimates. Here, we analyzed data generated by mobile phones and social media to estimate the weekly island-wide population at risk and within-island geographic heterogeneity of migration in Puerto Rico after Hurricane Maria. We compared these two data sources to population estimates derived from air travel records and census data. We observed a loss of population across all data sources throughout the study period, however, the magnitude and dynamics differ by data source. Census data predict a population loss of just over 129,000 from July 17 to July 2018, a 4% decrease; air travel data predicts a population loss of 168,295 for the same period of time, a 5% decrease; mobile phone based estimates predicts a loss of 235,375 form July 2017 to May 2018, an 8% decrease; and social media based estimates predict a loss of 476,779 from August 2017 to August 2018; a 17% decrease. On average, municipalities with smaller population size lost a bigger proportion of their population. Moreover, we infer that these municipalities experienced greater infrastructure damage as measured by the proportion of unknown locations stemming from these regions. Finally, our analysis measures a general shift of population from rural to urban centers within the island.

scholarly journals At-a-glance - What can social media tell us about the opioid crisis in Canada?

2018 ◽  
Vol 38 (6) ◽  
pp. 263-267 ◽  
Author(s):  
Semra Tibebu ◽  
Vicky C. Chang ◽  
Charles-Antoine Drouin ◽  
Wendy Thompson ◽  
Minh T. Do
Keyword(s):  
Public Health ◽  
Social Media ◽  
Public Health Practice ◽  
Social Media Analytics ◽  
Current Response ◽  
Health Practice ◽  
Opioid Use ◽  
Opioid Crisis ◽  
Community Stakeholders ◽  
Data Source

We explored social media as a potential data source for acquiring realtime information on opioid use and perceptions in Canada. Twitter messages were collected through a social media analytics platform between June 15, 2017, and July 13, 2017, and analyzed to identify recurring topics mentioned in the messages. Messages concerning the medical use of opioids as well as commentary on the Canadian government’s current response efforts to the opioid crisis were common. The findings of this study may help to inform public health practice and community stakeholders in their efforts to address the opioid crisis.

scholarly journals Data Sources for Drug Utilization Research in Brazil—DUR-BRA Study

2022 ◽  
Vol 12 ◽  
Author(s):  
Lisiane Freitas Leal ◽  
Claudia Garcia Serpa Osorio-de-Castro ◽  
Luiz Júpiter Carneiro de Souza ◽  
Felipe Ferre ◽  
Daniel Marques Mota ◽  
...  
Keyword(s):  
Adverse Event ◽  
Drug Utilization ◽  
Adverse Event Reporting ◽  
Data Sources ◽  
Event Reporting ◽  
Utilization Research ◽  
National Surveys ◽  
Reporting Systems ◽  
Data Source ◽  
Drug Utilization Research

Background: In Brazil, studies that map electronic healthcare databases in order to assess their suitability for use in pharmacoepidemiologic research are lacking. We aimed to identify, catalogue, and characterize Brazilian data sources for Drug Utilization Research (DUR).Methods: The present study is part of the project entitled, “Publicly Available Data Sources for Drug Utilization Research in Latin American (LatAm) Countries.” A network of Brazilian health experts was assembled to map secondary administrative data from healthcare organizations that might provide information related to medication use. A multi-phase approach including internet search of institutional government websites, traditional bibliographic databases, and experts’ input was used for mapping the data sources. The reviewers searched, screened and selected the data sources independently; disagreements were resolved by consensus. Data sources were grouped into the following categories: 1) automated databases; 2) Electronic Medical Records (EMR); 3) national surveys or datasets; 4) adverse event reporting systems; and 5) others. Each data source was characterized by accessibility, geographic granularity, setting, type of data (aggregate or individual-level), and years of coverage. We also searched for publications related to each data source.Results: A total of 62 data sources were identified and screened; 38 met the eligibility criteria for inclusion and were fully characterized. We grouped 23 (60%) as automated databases, four (11%) as adverse event reporting systems, four (11%) as EMRs, three (8%) as national surveys or datasets, and four (11%) as other types. Eighteen (47%) were classified as publicly and conveniently accessible online; providing information at national level. Most of them offered more than 5 years of comprehensive data coverage, and presented data at both the individual and aggregated levels. No information about population coverage was found. Drug coding is not uniform; each data source has its own coding system, depending on the purpose of the data. At least one scientific publication was found for each publicly available data source.Conclusions: There are several types of data sources for DUR in Brazil, but a uniform system for drug classification and data quality evaluation does not exist. The extent of population covered by year is unknown. Our comprehensive and structured inventory reveals a need for full characterization of these data sources.

Social Media in Occupational Health Nursing: Helpful or Harmful?

2020 ◽  
Vol 68 (9) ◽  
pp. 408-414
Author(s):  
Lee Anne Siegmund
Keyword(s):  
Health Care ◽  
Social Media ◽  
Occupational Health ◽  
Online Health Communities ◽  
Information Awareness ◽  
Health Studies ◽  
Health Communities ◽  
Health Nursing ◽  
Use Of Social Media ◽  
Privacy Breaches

Background: Social media, an online vehicle for communication and media sharing, is a growing phenomenon in many aspects of everyday life, including health care. We explored the ways occupational health nurses can use social media as a helpful resource as well as identified potential concerns associated with its use. Methods: A review of the literature was conducted between December 1, 2019, and April 10, 2020, using PubMed and Google Scholar. Key search terms included social media, social network, nurse or nursing, occupational health, and online health. Criteria for selection included studies with results on social media within health care, nursing, and/or occupational health. Studies were also included if the health effects of social media were addressed. Six additional studies that had been previously identified by hand searching were included. Findings: These findings support the use of social media in occupational health for encouraging participatory health care among employees. Occupational health nurses can also utilize social media for health information, online health communities, emergency communication, health education workshops, professional connections, and continuing education. However, awareness of safe social media practice is necessary due to the possibility of misinformation and privacy breaches. Conclusion/Application to Practice: Social media can be used for education and communication with employees and is a way to support employees with specific health conditions through participation in online health communities . Occupational health nurses can take advantage of the speed and accessibility of social media to reach large numbers of employees. It is also a useful tool for addressing many health concerns encountered by employees; however, careful sourcing of information, awareness of company policies, and other safe practices can help to ensure it is helpful and not harmful.

scholarly journals Quantifying the dynamics of migration after Hurricane Maria in Puerto Rico

2020 ◽  
Vol 117 (51) ◽  
pp. 32772-32778
Author(s):  
Rolando J. Acosta ◽  
Nishant Kishore ◽  
Rafael A. Irizarry ◽  
Caroline O. Buckee
Keyword(s):  
At Risk ◽  
Social Media ◽  
Puerto Rico ◽  
Census Data ◽  
Air Travel ◽  
Data Sources ◽  
Urban Centers ◽  
Population Loss ◽  
Data Source ◽  
Wide Population

Population displacement may occur after natural disasters, permanently altering the demographic composition of the affected regions. Measuring this displacement is vital for both optimal postdisaster resource allocation and calculation of measures of public health interest such as mortality estimates. Here, we analyzed data generated by mobile phones and social media to estimate the weekly island-wide population at risk and within-island geographic heterogeneity of migration in Puerto Rico after Hurricane Maria. We compared these two data sources with population estimates derived from air travel records and census data. We observed a loss of population across all data sources throughout the study period; however, the magnitude and dynamics differ by the data source. Census data predict a population loss of just over 129,000 from July 2017 to July 2018, a 4% decrease; air travel data predict a population loss of 168,295 for the same period, a 5% decrease; mobile phone-based estimates predict a loss of 235,375 from July 2017 to May 2018, an 8% decrease; and social media-based estimates predict a loss of 476,779 from August 2017 to August 2018, a 17% decrease. On average, municipalities with a smaller population size lost a bigger proportion of their population. Moreover, we infer that these municipalities experienced greater infrastructure damage as measured by the proportion of unknown locations stemming from these regions. Finally, our analysis measures a general shift of population from rural to urban centers within the island. Passively collected data provide a promising supplement to current at-risk population estimation procedures; however, each data source has its own biases and limitations.

scholarly journals Recent Advances in Using Natural Language Processing to Address Public Health Research Questions Using Social Media and ConsumerGenerated Data

2019 ◽  
Vol 28 (01) ◽  
pp. 208-217 ◽  
Author(s):  
Mike Conway ◽  
Mengke Hu ◽  
Wendy W. Chapman
Keyword(s):  
Public Health ◽  
Mental Health ◽  
Machine Learning ◽  
Social Media ◽  
Natural Language Processing ◽  
Language Processing ◽  
Online Health Communities ◽  
Machine Learning Methods ◽  
Health Communities ◽  
Health Applications

Objective: We present a narrative review of recent work on the utilisation of Natural Language Processing (NLP) for the analysis of social media (including online health communities) specifically for public health applications. Methods: We conducted a literature review of NLP research that utilised social media or online consumer-generated text for public health applications, focussing on the years 2016 to 2018. Papers were identified in several ways, including PubMed searches and the inspection of recent conference proceedings from the Association of Computational Linguistics (ACL), the Conference on Human Factors in Computing Systems (CHI), and the International AAAI (Association for the Advancement of Artificial Intelligence) Conference on Web and Social Media (ICWSM). Popular data sources included Twitter, Reddit, various online health communities, and Facebook. Results: In the recent past, communicable diseases (e.g., influenza, dengue) have been the focus of much social media-based NLP health research. However, mental health and substance use and abuse (including the use of tobacco, alcohol, marijuana, and opioids) have been the subject of an increasing volume of research in the 2016 - 2018 period. Associated with this trend, the use of lexicon-based methods remains popular given the availability of psychologically validated lexical resources suitable for mental health and substance abuse research. Finally, we found that in the period under review “modern" machine learning methods (i.e. deep neural-network-based methods), while increasing in popularity, remain less widely used than “classical" machine learning methods.

Protecting privacy and confidentiality in online health communities

2013 ◽  
Author(s):  
Jacqueline Amoozegar ◽  
Douglas Rupert ◽  
Jennifer Gard Read ◽  
Rebecca Moultrie ◽  
Kathryn Aikin ◽  
...  
Keyword(s):  
Online Health Communities ◽  
Health Communities ◽  
Privacy And Confidentiality
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