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.

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.

“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 Social Media and Patient Health Outcomes

2014 ◽  
Vol 23 (01) ◽  
pp. 195-198 ◽  
Author(s):  
N. Douali ◽  
P. Staccini ◽  
Keyword(s):  
Social Media ◽  
Health Informatics ◽  
Semantic Annotation ◽  
Practical Method ◽  
Consumer Health Informatics ◽  
Consumer Health ◽  
New Findings ◽  
Excellent Research ◽  
Patient Health ◽  
Health Communities

Summary Objectives: To provide a review of the current excellent research published in the field of Consumer Health Informatics. Method: We searched MEDLINE® and WEB OF SCIENCE® databases for papers published in 2013 in relation with Consumer Health Informatics. The authors identified 16 candidate best papers, which were then reviewed by four reviewers. Results: Five out of the 16 candidate papers were selected as best papers. One paper presents the key features of a system to automate the collection of web-based social media content for subsequent semantic annotation. This paper emphasizes the importance of mining social media to collect novel data from which new findings in drug abuse research were uncovered. The second paper presents a practical method to predict how a community structure would impact the spreading of information within the community. The third paper presents a method for improving the quality of online health communities. The fourth presents a new social network to allow the monitoring of the evolution of individuals’ health status and diagnostic deficiencies, difficulties or barriers in rehabilitation. The last paper reports on teenage patients’ perception on privacy and social media. Conclusion: Selected papers not only show the value of using social media in the medical field but how to use these media to detect emergent diseases or risks, inform patients, promote disease prevention, and follow patients’ opinion on healthcare resources.

Trajectory Pattern Mining: Methods and Applications

2014 ◽  
Vol 490-491 ◽  
pp. 1361-1367
Author(s):  
Xin Huang ◽  
Hui Juan Chen ◽  
Mao Gong Zheng ◽  
Ping Liu ◽  
Jing Qian
Keyword(s):  
Data Mining ◽  
Social Media ◽  
Pattern Mining ◽  
Data Sets ◽  
Trajectory Data ◽  
Data Mining Algorithms ◽  
Mining Methods ◽  
Trajectory Pattern ◽  
Mining Algorithms ◽  
Trajectory Pattern Mining

With the advent of location-based social media and locationacquisition technologies, trajectory data are becoming more and more ubiquitous in the real world. A lot of data mining algorithms have been successfully applied to trajectory data sets. Trajectory pattern mining has received a lot of attention in recent years. In this paper, we review the most inuential methods as well as typical applications within the context of trajectory pattern mining.

Depression detection using data mining algorithms from social media context

2021 ◽  
Author(s):  
Rama Krishna Tummala ◽  
E Bhuvaneswari ◽  
Tegil J. John ◽  
S.P. Karthi ◽  
K.P. Arjun
Keyword(s):  
Data Mining ◽  
Social Media ◽  
Data Mining Algorithms ◽  
Depression Detection ◽  
Using Data ◽  
Mining Algorithms

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.

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 Literature Study: Highway Traffic Management with Sentiment Analysis and Data Mining

2021 ◽  
Vol 1 (1) ◽  
pp. 27-31
Author(s):  
Nurul Khairina ◽  
Muhammad Khoiruddin Harahap
Keyword(s):  
Data Mining ◽  
Social Media ◽  
Sentiment Analysis ◽  
Traffic Congestion ◽  
Traffic Management ◽  
Road Traffic ◽  
Highway Traffic ◽  
Literature Study ◽  
Data Mining Algorithms ◽  
Mining Algorithms

In today's era, technology is growing rapidly, many of the latest technologies are in great demand by the Indonesian people, one of which is social media. Various social media such as Facebook, Twitter, Instagram, have become very popular applications for various ages, including teenagers, adults, and the elderly. Social media has a positive impact that can help people convey the latest information through posts on their respective accounts. Social media can disseminate information in a short time, this is why social media is an interesting application to research. The problem of road traffic congestion is strongly influenced by the number of vehicles that pass every day. A large number of private vehicles and public vehicles that pass greatly confuses the atmosphere of highway traffic. Congestion often occurs during working hours. Road congestion also often occurs when an unwanted incident occurs. Sentiment analysis algorithms and data mining algorithms can be combined to find information on traffic jams through social media such as Facebook, Twitter, Instagram, and other social media. The results show that sentiment analysis methods and data mining algorithms can be used to find information about current traffic jams through social media. The conclusion from this literature study can be seen that the K-Nearest Neighbor data mining algorithm is the best choice to overcome road traffic congestion, which will then be further developed in the form of highway traffic management modeling.

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