Reasons for Utilizing Telemedicine during and after the COVID-19 Pandemic: An Internet-Based International Study

Journal of Clinical Medicine, Год журнала: 2021, Номер 10(23), С. 5519 - 5519

Опубликована: Ноя. 25, 2021

The COVID-19 pandemic challenges healthcare services. Concomitantly, this had a stimulating effect on technological expansions related to telehealth and telemedicine. We sought elucidate the principal patients' reasons for using telemedicine during propensity use it thereafter. Our primary objective was identify of survey participants' disparate attitudes toward performed an online, multilingual 30-question 14 days March-April 2021, focusing perception usage their intent after pandemic. analyzed data attributes influencing built decision trees highlight most important variables. examined 473 answers: 272 from Israel, 87 Uruguay, 114 worldwide. Most participants were women (64.6%), married (63.8%) with 1-2 children (52.9%), living in urban areas (84.6%). Only third intended continue main findings are that expected substitution effect, technical proficiency, reduced queueing times, peer experience four major factors overall adoption Specifically, (1) participants, factor is implicit expectation such visit will be full substitute in-person appointment; (2) another affecting by patients proficiency comfort level common web-based tools, as social media, while seeking relevant medical information; (3) time saving can allow asynchronous communications, thereby reducing physical travel queuing times at clinic; finally (4) some have also indicated seems more attractive them watching family friends (peer experience) successfully.

Язык: Английский

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Monitoring User Opinions and Side Effects on COVID-19 Vaccines in the Twittersphere: Infodemiology Study of Tweets

Journal of Medical Internet Research, Год журнала: 2022, Номер 24(5), С. e35115 - e35115

Опубликована: Апрель 21, 2022

In the current phase of COVID-19 pandemic, we are witnessing most massive vaccine rollout in human history. Like any other drug, vaccines may cause unexpected side effects, which need to be investigated a timely manner minimize harm population. If not properly dealt with, effects also impact public trust vaccination campaigns carried out by national governments.Monitoring social media for early identification and understanding opinion on paramount importance ensure successful harmless rollout. The objective this study was create web portal monitor users vaccines, can offer tool journalists, scientists, alike visualize how general is reacting campaign.We developed analyze from Twitter, exploiting, among techniques, state-of-the-art system adverse drug events media; natural language processing models sentiment analysis; statistical tools; open-source databases trending hashtags, news articles, their factuality. All modules displayed through an open portal.A set 650,000 tweets collected analyzed ongoing process that initiated December 2020. results analysis made (updated daily), together with tools data. data provide insights about its change over time. For example, show high tendency only share reliable sources when discussing (98% shared URLs). Twitter toward negative/neutral; however, able record fluctuations attitude specific correspondence (eg, new outbreaks). coverage had discussed topics. To further investigate point, performed more in-depth regarding AstraZeneca vaccine. We observed blood clot-related suddenly shifted topic discussions both vaccines. This became particularly evident visualizing frequently symptoms comparing them month month.We present connected display some key aspects public's reaction provides overview opinions Twittersphere graphic representations, offering extraction suspected deep learning model.

Язык: Английский

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Role of Artificial Intelligence for Analysis of COVID-19 Vaccination-Related Tweets: Opportunities, Challenges, and Future Trends

Mathematics, Год журнала: 2022, Номер 10(17), С. 3199 - 3199

Опубликована: Сен. 5, 2022

Pandemics and infectious diseases are overcome by vaccination, which serves as a preventative measure. Nevertheless, vaccines also raise public concerns; apprehension doubts challenge the acceptance of new vaccines. COVID-19 received similarly hostile reaction from public. In addition, misinformation social media, contradictory comments medical experts, reports worse reactions led to negative vaccine perceptions. Many researchers analyzed people’s varying sentiments regarding using artificial intelligence (AI) approaches. This study is first attempt review role AI approaches in vaccination-related sentiment analysis. For this purpose, insights publications gathered that analyze (a) used develop analysis tools, (b) major sources data, (c) available data sources, (d) perception vaccine. Analysis suggests perception-related tweets predominantly TextBlob. Moreover, large extent, have employed Latent Dirichlet Allocation model for topic modeling Twitter data. Another pertinent discovery made our variation across different regions. We anticipate systematic will serve an all-in-one source research community determining right technique their requirements. Our findings provide insight into assist them future work current domain.

Язык: Английский

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Accident & Emergency Informatics and One Digital Health

Yearbook of Medical Informatics, Год журнала: 2022, Номер 31(01), С. 040 - 046

Опубликована: Июнь 2, 2022

Climate changes are the major challenge in public and individual health, as they modify ecosystem yield contagious diseases from animal to human. Furthermore, we notice rapid development of elderly, changing population demographic. These critical measures have imposed economical costs, require trained personnel, reduce healthcare systems' performances.

Язык: Английский

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The Role of Influential Actors in Fostering the Polarized COVID-19 Vaccine Discourse on Twitter: Mixed Methods of Machine Learning and Inductive Coding

JMIR Infodemiology, Год журнала: 2022, Номер 2(1), С. e34231 - e34231

Опубликована: Март 31, 2022

Since COVID-19 vaccines became broadly available to the adult population, sharp divergences in uptake have emerged along partisan lines. Researchers indicated a polarized social media presence contributing spread of mis- or disinformation as being responsible for these growing gaps uptake.The major aim this study was investigate role influential actors context community structures and discourse related vaccine conversations on Twitter that prior rollout general population discuss implications promotion policy.We collected tweets between July 1, 2020, 31, time when attitudes toward were forming but before widely public. Using network analysis, we identified different naturally emerging communities based their internal information sharing. A PageRank algorithm used quantitively measure level "influentialness" accounts identifying "influencers," followed by coding them into actor categories. Inductive conducted describe discourses shared each 7 communities.Twitter highly polarized, with occupying separate "clusters." The antivaccine cluster most densely connected group. Among 100 actors, medical experts outnumbered both activist skeptics conspiracy theorists. Scientists largely absent from conservative network, sentiment especially salient among political right. Conversations lines, "trust" manipulated advantage actors.These findings are informative designing improved communication strategies be delivered incorporating actors. Although polarization echo chamber effect not new media, it concerning observe health during development process.

Язык: Английский

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Emotions and Incivility in Vaccine Mandate Discourse: Natural Language Processing Insights

JMIR Infodemiology, Год журнала: 2022, Номер 2(2), С. e37635 - e37635

Опубликована: Авг. 30, 2022

Despite vaccine availability, hesitancy has inhibited public health officials' efforts to mitigate the COVID-19 pandemic in United States. Although some US elected officials have responded by issuing mandates, others amplified broadcasting messages that minimize efficacy. The politically polarized nature of information on social media given rise incivility, wherein attitudes often hinge more political ideology than science.

Язык: Английский

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Early Detection and Control of the Next Epidemic Wave Using Health Communications: Development of an Artificial Intelligence-Based Tool and Its Validation on COVID-19 Data from the US

International Journal of Environmental Research and Public Health, Год журнала: 2022, Номер 19(23), С. 16023 - 16023

Опубликована: Ноя. 30, 2022

Social media networks highly influence on a broad range of global social life, especially in the context pandemic. We developed mathematical model with computational tool, called EMIT (Epidemic and Media Impact Tool), to detect control pandemic waves, using mainly topics relevance spread. Using EMIT, we analyzed health-related communications for early prediction, detection, an outbreak. is artificial intelligence-based tool supporting health communication policy makers decisions. Thus, based historical data, trends disease spread, offers predictive estimation public interventions such as media-based campaigns. have validated real world data combining COVID-19 US from Twitter. demonstrated high level performance predicting next epidemiological wave (AUC = 0.909, F1 0.899).

Язык: Английский

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Multisite development and validation of machine learning models to predict severe outcomes and guide decision‐making for emergency department patients with influenza

Journal of the American College of Emergency Physicians Open, Год журнала: 2024, Номер 5(2)

Опубликована: Март 18, 2024

Millions of Americans are infected by influenza annually. A minority seek care in the emergency department (ED) and, those, only a limited number experience severe disease or death. ED clinicians must distinguish those at risk for deterioration from who can be safely discharged.

Язык: Английский

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Stance and Sentiment Analysis of Health-related Tweets with Data Augmentation

Journal of Scientific & Industrial Research, Год журнала: 2024, Номер 83(4)

Опубликована: Апрель 1, 2024

Common social media platforms like Twitter are important as up-to-date information sources for several monitoring purposes, including instant public health monitoring. In this sense, large volumes of health-related posts (such tweets on the COVID-19 pandemic) have been produced recently, and ready to be analyzed facilitate decision making. paper, joint stance detection sentiment analysis about vaccination was performed, in order showcase contribution different machine learning deep techniques equipped with data augmentation. Training test tweet datasets compiled annotated both next, training dataset is extended using an automatic augmentation technique increase its size. Experiments classifiers performed automated analyses, during training. The adopted study cope scarcity problems research leads better performance rates domain analysis. Comparative evaluations also a publicly-available tool. dataset, along approaches, evaluation results significant informatics, because, they estimation community towards which has concern. Therefore, decision-makers can extensively readily benefit from findings resources current study.

Язык: Английский

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Reshaping Epistemic Identity on X/Twitter: A Spanish-English Contrast

Springer eBooks, Год журнала: 2024, Номер unknown, С. 53 - 90

Опубликована: Янв. 1, 2024

Язык: Английский

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