scholarly journals Change in Threads on Twitter Regarding Influenza, Vaccines, and Vaccination During the COVID-19 Pandemic: Artificial Intelligence–Based Infodemiology Study (Preprint)

2021 ◽  
Author(s):  
Arriel Benis ◽  
Anat Chatsubi ◽  
Eugene Levner ◽  
Shai Ashkenazi
Keyword(s):  
Artificial Intelligence ◽  
United States ◽  
Machine Learning ◽  
Health Care ◽  
Social Media ◽  
Influenza Vaccination ◽  
Information Source ◽  
The United States ◽  
Influenza Vaccines ◽  
Clinical Aspects

BACKGROUND Discussions of health issues on social media are a crucial information source reflecting real-world responses regarding events and opinions. They are often important in public health care, since these are influencing pathways that affect vaccination decision-making by hesitant individuals. Artificial intelligence methodologies based on internet search engine queries have been suggested to detect disease outbreaks and population behavior. Among social media, Twitter is a common platform of choice to search and share opinions and (mis)information about health care issues, including vaccination and vaccines. OBJECTIVE Our primary objective was to support the design and implementation of future eHealth strategies and interventions on social media to increase the quality of targeted communication campaigns and therefore increase influenza vaccination rates. Our goal was to define an artificial intelligence–based approach to elucidate how threads in Twitter on influenza vaccination changed during the COVID-19 pandemic. Such findings may support adapted vaccination campaigns and could be generalized to other health-related mass communications. METHODS The study comprised the following 5 stages: (1) collecting tweets from Twitter related to influenza, vaccines, and vaccination in the United States; (2) data cleansing and storage using machine learning techniques; (3) identifying terms, hashtags, and topics related to influenza, vaccines, and vaccination; (4) building a dynamic folksonomy of the previously defined vocabulary (terms and topics) to support the understanding of its trends; and (5) labeling and evaluating the folksonomy. RESULTS We collected and analyzed 2,782,720 tweets of 420,617 unique users between December 30, 2019, and April 30, 2021. These tweets were in English, were from the United States, and included at least one of the following terms: “flu,” “influenza,” “vaccination,” “vaccine,” and “vaxx.” We noticed that the prevalence of the terms vaccine and vaccination increased over 2020, and that “flu” and “covid” occurrences were inversely correlated as “flu” disappeared over time from the tweets. By combining word embedding and clustering, we then identified a folksonomy built around the following 3 topics dominating the content of the collected tweets: “health and medicine (biological and clinical aspects),” “protection and responsibility,” and “politics.” By analyzing terms frequently appearing together, we noticed that the tweets were related mainly to COVID-19 pandemic events. CONCLUSIONS This study focused initially on vaccination against influenza and moved to vaccination against COVID-19. Infoveillance supported by machine learning on Twitter and other social media about topics related to vaccines and vaccination against communicable diseases and their trends can lead to the design of personalized messages encouraging targeted subpopulations’ engagement in vaccination. A greater likelihood that a targeted population receives a personalized message is associated with higher response, engagement, and proactiveness of the target population for the vaccination process.

scholarly journals Change in Threads on Twitter Regarding Influenza, Vaccines, and Vaccination During the COVID-19 Pandemic: Artificial Intelligence–Based Infodemiology Study

10.2196/31983 ◽  
2021 ◽  
Vol 1 (1) ◽  
pp. e31983
Author(s):  
Arriel Benis ◽  
Anat Chatsubi ◽  
Eugene Levner ◽  
Shai Ashkenazi
Keyword(s):  
Artificial Intelligence ◽  
United States ◽  
Machine Learning ◽  
Health Care ◽  
Social Media ◽  
Influenza Vaccination ◽  
Information Source ◽  
The United States ◽  
Influenza Vaccines ◽  
Clinical Aspects

Background Discussions of health issues on social media are a crucial information source reflecting real-world responses regarding events and opinions. They are often important in public health care, since these are influencing pathways that affect vaccination decision-making by hesitant individuals. Artificial intelligence methodologies based on internet search engine queries have been suggested to detect disease outbreaks and population behavior. Among social media, Twitter is a common platform of choice to search and share opinions and (mis)information about health care issues, including vaccination and vaccines. Objective Our primary objective was to support the design and implementation of future eHealth strategies and interventions on social media to increase the quality of targeted communication campaigns and therefore increase influenza vaccination rates. Our goal was to define an artificial intelligence–based approach to elucidate how threads in Twitter on influenza vaccination changed during the COVID-19 pandemic. Such findings may support adapted vaccination campaigns and could be generalized to other health-related mass communications. Methods The study comprised the following 5 stages: (1) collecting tweets from Twitter related to influenza, vaccines, and vaccination in the United States; (2) data cleansing and storage using machine learning techniques; (3) identifying terms, hashtags, and topics related to influenza, vaccines, and vaccination; (4) building a dynamic folksonomy of the previously defined vocabulary (terms and topics) to support the understanding of its trends; and (5) labeling and evaluating the folksonomy. Results We collected and analyzed 2,782,720 tweets of 420,617 unique users between December 30, 2019, and April 30, 2021. These tweets were in English, were from the United States, and included at least one of the following terms: “flu,” “influenza,” “vaccination,” “vaccine,” and “vaxx.” We noticed that the prevalence of the terms vaccine and vaccination increased over 2020, and that “flu” and “covid” occurrences were inversely correlated as “flu” disappeared over time from the tweets. By combining word embedding and clustering, we then identified a folksonomy built around the following 3 topics dominating the content of the collected tweets: “health and medicine (biological and clinical aspects),” “protection and responsibility,” and “politics.” By analyzing terms frequently appearing together, we noticed that the tweets were related mainly to COVID-19 pandemic events. Conclusions This study focused initially on vaccination against influenza and moved to vaccination against COVID-19. Infoveillance supported by machine learning on Twitter and other social media about topics related to vaccines and vaccination against communicable diseases and their trends can lead to the design of personalized messages encouraging targeted subpopulations’ engagement in vaccination. A greater likelihood that a targeted population receives a personalized message is associated with higher response, engagement, and proactiveness of the target population for the vaccination process.

scholarly journals Brief report: Influenza vaccination and health care workers in the united states

2006 ◽  
Vol 21 (2) ◽  
pp. 181-184 ◽  
Author(s):  
William D. King ◽  
Steffie J. Woolhandler ◽  
Arleen F. Brown ◽  
Luohua Jiang ◽  
Kristine Kevorkian ◽  
...  
Keyword(s):  
United States ◽  
Health Care ◽  
Influenza Vaccination ◽  
Health Care Workers ◽  
The United States ◽  
Care Workers

scholarly journals Identification and Management of Sepsis in the Interventional Radiology Patient

2017 ◽  
Vol 1 ◽  
pp. 3
Author(s):  
Jacqueline Murtha ◽  
Vinit Khanna ◽  
Talia Sasson ◽  
Devang Butani
Keyword(s):  
United States ◽  
Health Care ◽  
Septic Shock ◽  
Interventional Radiology ◽  
Hospital Setting ◽  
The United States ◽  
Clinical Aspects ◽  
Inhospital Mortality ◽  
United States Population ◽  
Hospital Acquired

Sepsis is frequently encountered in the hospital setting and can be community-acquired, health-care-associated, or hospital-acquired. The annual incidence of sepsis in the United States population ranges from 300 to 1031 per 100,000 and is increasing by 13% annually. There is an associated inhospital mortality of 10% for sepsis and >40% for septic shock. Interventional radiology is frequently called on to treat patients with sepsis, and in rarer circumstances, interventional radiologists themselves may cause sepsis. Thus, it is essential for interventional radiologists to be able to identify and manage septic patients to reduce sepsis-related morbidity and mortality. The purpose of this paper is to outline procedures most likely to cause sepsis and delineate important clinical aspects of identifying and managing septic patients.

Conclusion

2020 ◽  
pp. 97-102
Author(s):  
Benjamin Wiggins
Keyword(s):  
Artificial Intelligence ◽  
United States ◽  
Machine Learning ◽  
Risk Assessment ◽  
Big Data ◽  
The United States ◽  
Blaise Pascal ◽  
Division Problem ◽  
The World

Can risk assessment be made fair? The conclusion of Calculating Race returns to actuarial science’s foundations in probability. The roots of probability rest in a pair of problems posed to Blaise Pascal and Pierre de Fermat in the summer of 1654: “the Dice Problem” and “the Division Problem.” From their very foundation, the mathematics of probability offered the potential not only to be used to gain an advantage (as in the case of the Dice Problem), but also to divide material fairly (as in the case of the Division Problem). As the United States and the world enter an age driven by Big Data, algorithms, artificial intelligence, and machine learning and characterized by an actuarialization of everything, we must remember that risk assessment need not be put to use for individual, corporate, or government advantage but, rather, that it has always been capable of guiding how to distribute risk equitably instead.

scholarly journals Threat, Coping, and Social Distance Adherence During COVID-19: Cross-Continental Comparison Using an Online Cross-Sectional Survey

10.2196/23019 ◽  
2020 ◽  
Vol 22 (11) ◽  
pp. e23019
Author(s):  
Abrar Al-Hasan ◽  
Jiban Khuntia ◽  
Dobin Yim
Keyword(s):  
United States ◽  
Social Media ◽  
South Korea ◽  
Information Source ◽  
The United States ◽  
Seemingly Unrelated Regression ◽  
Social Distancing ◽  
Unrelated Regression ◽  
And Coping ◽  
Appraisal Processes

Background Social distancing is an effective preventative policy for COVID-19 that is enforced by governments worldwide. However, significant variations are observed in adherence to social distancing across individuals and countries. Due to the lack of treatment, rapid spread, and prevalence of COVID-19, panic and fear associated with the disease causes great stress. Subsequent effects will be a variation around the coping and mitigation strategies for different individuals following different paths to manage the situation. Objective This study aims to explore how threat and coping appraisal processes work as mechanisms between information and citizens’ adherence to COVID-19–related recommendations (ie, how the information sources and social media influence threat and coping appraisal processes with COVID-19 and how the threat and coping appraisal processes influence adherence to policy guidelines). In addition, this study aims to explore how citizens in three different countries (the United States, Kuwait, and South Korea), randomly sampled, are effectively using the mechanisms. Methods Randomly sampled online survey data collected by a global firm in May 2020 from 162 citizens of the United States, 185 of Kuwait, and 71 of South Korea were analyzed, resulting in a total sample size of 418. A seemingly unrelated regression model, controlling for several counterfactuals, was used for analysis. The study’s focal estimated effects were compared across the three countries using the weighted distance between the parameter estimates. Results The seemingly unrelated regression model estimation results suggested that, overall, the intensity of information source use for the COVID-19 pandemic positively influenced the threat appraisal for the disease (P<.001). Furthermore, the intensity of social media use for the COVID-19 pandemic positively influenced the coping appraisal for the disease (P<.001). Higher COVID-19 threat appraisal had a positive effect on social distancing adherence (P<.001). Higher COVID-19 coping appraisal had a positive effect on social distancing adherence (P<.001). Higher intensity of COVID-19 knowledge positively influenced social distancing adherence (P<.001). There were country-level variations. Broadly, we found that the United States had better results than South Korea and Kuwait in leveraging the information to threat and coping appraisal to the adherence process, indicating that individuals in countries like the United States and South Korea may be more pragmatic to appraise the situation before making any decisions. Conclusions This study’s findings suggest that the mediation of threat and coping strategies are essential, in varying effects, to shape the information and social media strategies for adherence outcomes. Accordingly, coordinating public service announcements along with information source outlets such as mainstream media (eg, TV and newspaper) as well as social media (eg, Facebook and Twitter) to inform citizens and, at the same time, deliver balanced messages about the threat and coping appraisal is critical in implementing a staggered social distancing and sheltering strategy.

scholarly journals Coping With and Adapting to COVID-19 in Rural United States and Canada

2021 ◽  
Vol 102 (1) ◽  
pp. 78-90
Author(s):  
Donna Wang ◽  
Jill M. Chonody ◽  
Kathryn Krase ◽  
Leina Luzuriaga
Keyword(s):  
United States ◽  
Health Care ◽  
Social Media ◽  
Rural Areas ◽  
Online Survey ◽  
The United States ◽  
Small Towns ◽  
Cross Sectional ◽  
Personal Impact ◽  
Levels Of Support

Guidelines aimed at slowing the spread of COVID-19 resulted in major changes in people’s lives. A cross-sectional online survey, completed by 1,405 adults in Canada and the United States in June 2020, found respondents from rural areas/small towns reported better coping and adjustment (i.e., less use of substances for support), less personal impact, less life disruption, and fewer challenges with transportation and health care, than urban and suburban respondents. Those in rural areas were less likely to use the newspaper, but more likely to use social media, for information. Finally, rural respondents reported higher levels of support for their national leadership’s response to the pandemic. The needs and strengths of rural areas, as well as approaches to serve rural areas are discussed.

scholarly journals Should Health‐Care Providers in the United States Have Access to Influenza Vaccines Formulated for the Southern Hemisphere?

2008 ◽  
Vol 15 (6) ◽  
pp. 442-446 ◽  
Author(s):  
Raymond A. Strikas ◽  
Phyllis E. Kozarsky ◽  
Christie Reed ◽  
Brian K. Kapella ◽  
David O. Freedman
Keyword(s):  
United States ◽  
Health Care ◽  
Southern Hemisphere ◽  
Health Care Providers ◽  
The United States ◽  
Influenza Vaccines ◽  
Care Providers

scholarly journals A Chronological and Geographical Analysis of Personal Reports of COVID-19 on Twitter

2020 ◽  
Author(s):  
Ari Z. Klein ◽  
Arjun Magge ◽  
Karen O’Connor ◽  
Haitao Cai ◽  
Davy Weissenbacher ◽  
...  
Keyword(s):  
United States ◽  
Machine Learning ◽  
Social Media ◽  
Language Processing ◽  
Personal Information ◽  
The United States ◽  
Social Media Mining ◽  
Learning Framework ◽  
Early Indication ◽  
Media Mining

ABSTRACTThe rapidly evolving outbreak of COVID-19 presents challenges for actively monitoring its spread. In this study, we assessed a social media mining approach for automatically analyzing the chronological and geographical distribution of users in the United States reporting personal information related to COVID-19 on Twitter. The results suggest that our natural language processing and machine learning framework could help provide an early indication of the spread of COVID-19.

scholarly journals Social Media Engagement and Influenza Vaccination During the COVID-19 Pandemic: Cross-sectional Survey Study

10.2196/25977 ◽  
2021 ◽  
Vol 23 (3) ◽  
pp. e25977 ◽  
Author(s):  
Arriel Benis ◽  
Anna Khodos ◽  
Sivan Ran ◽  
Eugene Levner ◽  
Shai Ashkenazi
Keyword(s):  
Health Care ◽  
Social Media ◽  
Influenza Vaccination ◽  
Influenza Vaccines ◽  
Information Searching ◽  
Vaccination Rates ◽  
Media Usage ◽  
Related Information ◽  
Online Social Media ◽  
Social Media Engagement

Background Vaccines are one of the most important achievements of modern medicine. However, their acceptance is only partial, with vaccine hesitancy and refusal representing a major health threat. Influenza vaccines have low compliance since repeated, annual vaccination is required. Influenza vaccines stimulate discussions both in the real world and online. Social media is currently a significant source of health and medical information. Elucidating the association between social media engagement and influenza vaccination is important and may be applicable to other vaccines, including ones against COVID-19. Objective The goal of this study is to characterize profiles of social media engagement regarding the influenza vaccine and their association with knowledge and compliance in order to support improvement of future web-associated vaccination campaigns. Methods A weblink to an online survey in Hebrew was disseminated over social media and messaging platforms. The survey answers were collected during April 2020. Anonymous and volunteer participants aged 21 years and over answered 30 questions related to sociodemographics; social media usage; influenza- and vaccine-related knowledge and behavior; health-related information searching, its reliability, and its influence; and COVID-19-related information searching. A univariate descriptive data analysis was performed, followed by multivariate analysis via building a decision tree to define the most important attributes associated with vaccination compliance. Results A total of 213 subjects responded to the survey, of whom 207 were included in the analysis; the majority of the respondents were female, were aged 21 to 40 years, had 1 to 2 children, lived in central Israel, were secular Israeli natives, had higher education, and had a salary close to the national average. Most respondents (128/207, 61.8%) were not vaccinated against influenza in 2019 and used social media. Participants that used social media were younger, secular, and living in high-density agglomerations and had lower influenza vaccination rates. The perceived influence and reliability of the information on social media about COVID-19 were generally similar to those perceptions about influenza. Conclusions Using social media is negatively linked to compliance with seasonal influenza vaccination in this study. A high proportion of noncompliant individuals can lead to increased consumption of health care services and can, therefore, overload these health services. This is particularly crucial with a concomitant outbreak, such as COVID-19. Health care professionals should use improved and targeted health communication campaigns with the aid of experts in social media. Targeted communication, based on sociodemographic factors and personalized social media usage, might increase influenza vaccination rates and compliance with other vaccines as well.

Artificial Intelligence (AI), Disability, and Telemedicine/Telehealth

2021 ◽  
pp. 193-223
Author(s):  
Sharon L. Burton
Keyword(s):  
Artificial Intelligence ◽  
United States ◽  
Health Care ◽  
Best Practices ◽  
Program Development ◽  
The United States ◽  
Academic Program ◽  
Academic Programs ◽  
Community Based ◽  
Academic Courses

Research reveals the significance of artificial intelligence's applicability for disability through telemedicine/telehealth education for advancing health care in rural, remote, and underserved locations. Improperly researched requirements, failure to include artificial intelligence (AI), and skewed monetary knowledge are derailing components for academic programs in the United States. Artificial intelligence is a key component to pinpoint inadequacies and drive them out of telemedicine/telehealth educational clinical processes and, as an outcome, help diminish costs and enhance outcomes for learners and administrators. This chapter revealed information for developing best practices, which will lead to the development of a series of academic courses for a community-based telehealth program at a medium-sized telehealth organization based out of Virginia. This research offers to practitioners, learners, and academicians academic program development suggestions for meeting a process improvement initiative.

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