Extracting factors associated with vaccination from Twitter data and mapping to behavioral models.

Social media platform, particularly Twitter, is a rich data source that allows monitoring of public opinions and attitudes toward vaccines.Established behavioral models like the 5C psychological antecedents model and the Health Belief Model (HBM) provide a well-structured framework for analyzing shifts in vaccine-related behavior. This study examines if the extracted data from Twitter contains valuable insights regarding public attitudes toward vaccines and can be mapped to two behavioral models. This study focuses on the Arab population, and a search was carried out on Twitter using: ' تلقيحي OR تطعيم OR تطعيمات OR لقاح OR لقاحات' for two years from January 2020 to January 2022. Then, BERTopicmodeling was applied, and several topics were extracted. Finally, the topics were manually mapped to the factors of the 5C model and HBM. 1,068,466 unique users posted 3,368,258 vaccine-related tweets in Arabic. Topic modeling generated 25 topics, which were mapped to the 15 factors of the 5C model and HBM. Among the users, 32.87%were male, and 18.06% were female. A significant 55.77% of the users were from the MENA (Middle East and North Africa) region. Twitter users were more inclined to accept vaccines when they trusted vaccine safety and effectiveness, but vaccine hesitancy increased due to conspiracy theories and misinformation. The association of topics with these theoretical frameworks reveals the availability and diversity of Twitter data that can predict behavioral change toward vaccines. It allows the preparation of timely and effective interventions for vaccination programs compared to traditional methods.

Longitudinal analysis of behavioral factors and techniques used to identify vaccine hesitancy among Twitter users: Scoping review.

While vaccines have played a pivotal role in the fight against infectious diseases, individuals engage in online resources to find vaccine-related support and information. The benefits and consequences of these online peers are unclear and mainly cause a behavioral shift in user sentiment toward vaccination. This scoping review aims to identify the community and individual factors that longitudinally influence public behavior toward vaccination. The secondary aim is to gain insight into techniques and methodologies used to extract these factors from Twitter data. We followed PRISMA-ScR guidelines to search various online repositories. From this search process, a total of 28 most relevant articles out of 705 relevant studies. Three main themes emerged including individual and community factors influencing public attitude toward vaccination, and techniques employed to identify these factors. Anti-vax, Pro-vax, and neutral are the major communities, while misinformation, vaccine campaign, and user demographics are the common individual factors assessed during this reviewing process. Twitter user sentiment (positive, negative, and neutral) and emotions (fear, trust, sadness) were also discussed to identify the intentions to accept or refuse vaccines. SVM, LDA, BERT are the techniques used for topic modeling, while Louvain, NodeXL, and Infomap algorithms are used for community detection. This research is notable for being the first systematic review that emphasizes the dearth of longitudinal studies and the methodological and underlying practical constraints underpinning the lucrative implementation of an explainable and longitudinal behavior analysis system. Moreover, new possible research directions are suggested for the researchers to perform accurate human behavior analysis.

Multimodal deep learning for liver cancer applications: a scoping review.

Background: Hepatocellular carcinoma is a malignant neoplasm of the liver and a leading cause of cancer-related deaths worldwide. The multimodal data combines several modalities, such as medical images, clinical parameters, and electronic health record (EHR) reports, from diverse sources to accomplish the diagnosis of liver cancer. The introduction of deep learning models with multimodal data can enhance the diagnosis and improve physicians' decision-making for cancer patients. Objective: This scoping review explores the use of multimodal deep learning techniques (i.e., combining medical images and EHR data) in diagnosing and prognosis of hepatocellular carcinoma (HCC) and cholangiocarcinoma (CCA). Methodology: A comprehensive literature search was conducted in six databases along with forward and backward references list checking of the included studies. PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) extension for scoping review guidelines were followed for the study selection process. The data was extracted and synthesized from the included studies through thematic analysis. Results: Ten studies were included in this review. These studies utilized multimodal deep learning to predict and diagnose hepatocellular carcinoma (HCC), but no studies examined cholangiocarcinoma (CCA). Four imaging modalities (CT, MRI, WSI, and DSA) and 51 unique EHR records (clinical parameters and biomarkers) were used in these studies. The most frequently used medical imaging modalities were CT scans followed by MRI, whereas the most common EHR parameters used were age, gender, alpha-fetoprotein AFP, albumin, coagulation factors, and bilirubin. Ten unique deep-learning techniques were applied to both EHR modalities and imaging modalities for two main purposes, prediction and diagnosis. Conclusion: The use of multimodal data and deep learning techniques can help in the diagnosis and prediction of HCC. However, there is a limited number of works and available datasets for liver cancer, thus limiting the overall advancements of AI for liver cancer applications. Hence, more research should be undertaken to explore further the potential of multimodal deep learning in liver cancer applications.

Influences of social media usage on public attitudes and behavior toward COVID-19 vaccine in the Arab world.

BACKGROUND: Vaccination programs are effective only when a significant percentage of people are vaccinated. Social media usage is arguably one of the factors affecting public attitudes toward vaccines. OBJECTIVE: This study aims to identify if the social media usage factors can predict Arab people's attitudes and behavior toward the COVID-19 vaccines. METHODS: An online survey was conducted in the Arab countries, and 217 Arab nationals participated in this study. Logistic regression was applied to identify what demographics and social media usage factors predict public attitudes and behavior toward the COVID-19 vaccines. RESULTS: Of the 217 participants, 56.2% (n = 122) were willing to get the vaccines, and 41.5% (n = 90) were hesitant. This study shows that none of the social media usage factors were significant enough to predict the actual vaccine acceptance behavior. However, some social media usage factors could predict public attitudes toward the COVID-19 vaccines. For example, compared to infrequent social media users, frequent social media users were 2.85 times more likely to agree that the risk of COVID-19 was being exaggerated (OR = 2.85, 95% CI = 0.86-9.45, p = .046). On the other hand, participants with more trust in vaccine information shared by their contacts were less likely to agree that decision-makers had ensured the safety of vaccines (OR = 0.528, 95% CI = 0.276-1.012, p = .05). CONCLUSION: Information shared on social media may affect public attitudes toward COVID-19 vaccines. Therefore, disseminating correct and validated information about the COVID-19 vaccines on social media is important to increase public trust and counter the impact of incorrect misinformation.

Public attitudes on social media toward vaccination before and during the COVID-19 pandemic.

With the success of COVID-19 vaccines in clinical trials, vaccination programs are being administered for the population with the hopes of herd immunity. However, the success of any vaccination program depends on the percentage of people willing to get vaccination which is influenced by social, economic, demographic, and vaccine-specific factors. Thus, it is important to understand public attitudes and perceptions toward vaccination. This study aims to measure public attitude toward vaccines and vaccinations before and during the COVID-19 pandemic, using public data from Twitter. A total of 880,586 tweets for 57,529 unique users were included in the study. Most of the tweets were posted in five languages: French, English, Swedish, Dutch, and Italian. These tweets were divided into two time periods: before COVID-19 (T1) and during COVID-19 (T2). This study observed the shift in the sentiments of the public attitude toward vaccines before and during COVID-19 pandemic. Both positive and negative shifts in sentiments were observed for the users of various languages but shifts toward positive sentiments were more prominent during the COVID-19 pandemic.

Customized and Automated Machine Learning-Based Models for Diabetes Type 2 Classification.

This study aims to develop models to accurately classify patients with type 2 diabetes using the Practice Fusion dataset. We use Random Forest (RF), Support Vector Classifier (SVC), AdaBoost classifier, an ensemble model, and automated machine learning (AutoML) model. We compare the performance of all models in a five-fold cross-validation scheme using four evaluation measures. Experimental results demonstrate that the AutoML model outperformed individual and ensemble models in all evaluation measures.

Vaccine Rollout and Shift in Public Sentiment: Twitter-Based Surveillance Study.

This study aims to find out the variation of Twitter users' sentiment before and after the COVID-19 vaccine rollout. We analyzed all COVID-19 related tweets posted on Twitter within two timeframes: September 2020 (T1) and March 2021 (T2). A total of 3 million tweets from over 132 thousand users were analyzed. We then categorized the users into two groups whose overall sentiment shifted positively or negatively from T1 to T2. Our analysis showed that 27% of users' sentiment shifted from T1 to T2 positively and the users were more confident about vaccine safety and efficacy. Users reported positive sentiments about travelling and the easing of lockdown measures. Also, 20.4% of the users' sentiment shifted negatively from T1 to T2. This group of Twitter users were more concerned about the adverse side effects of the vaccine, the pace of vaccine development as well as the emerging novel coronavirus variants. Interestingly, over half of the users' overall sentiment remained the same in both periods of T1 and T2, indicating indifference about vaccine rollout. We believe that our analysis will support the exploration of public reaction to COVID-19 vaccine rollout and assess policy makers' decision to combat the pandemic.

The role of generative adversarial networks in brain MRI: a scoping review.

The performance of artificial intelligence (AI) for brain MRI can improve if enough data are made available. Generative adversarial networks (GANs) showed a lot of potential to generate synthetic MRI data that can capture the distribution of real MRI. Besides, GANs are also popular for segmentation, noise removal, and super-resolution of brain MRI images. This scoping review aims to explore how GANs methods are being used on brain MRI data, as reported in the literature. The review describes the different applications of GANs for brain MRI, presents the most commonly used GANs architectures, and summarizes the publicly available brain MRI datasets for advancing the research and development of GANs-based approaches. This review followed the guidelines of PRISMA-ScR to perform the study search and selection. The search was conducted on five popular scientific databases. The screening and selection of studies were performed by two independent reviewers, followed by validation by a third reviewer. Finally, the data were synthesized using a narrative approach. This review included 139 studies out of 789 search results. The most common use case of GANs was the synthesis of brain MRI images for data augmentation. GANs were also used to segment brain tumors and translate healthy images to diseased images or CT to MRI and vice versa. The included studies showed that GANs could enhance the performance of AI methods used on brain MRI imaging data. However, more efforts are needed to transform the GANs-based methods in clinical applications.

A Scoping Review to Find Out Worldwide COVID-19 Vaccine Hesitancy and Its Underlying Determinants.

BACKGROUND: The current crisis created by the coronavirus pandemic is impacting all facets of life. Coronavirus vaccines have been developed to prevent coronavirus infection and fight the pandemic. Since vaccines might be the only way to prevent and stop the spread of coronavirus. The World Health Organization (WHO) has already approved several vaccines, and many countries have started vaccinating people. Misperceptions about vaccines persist despite the evidence of vaccine safety and efficacy. OBJECTIVES: To explore the scientific literature and find the determinants for worldwide COVID-19 vaccine hesitancy as reported in the literature. METHODS: PRISMA Extension for Scoping Reviews (PRISMA-ScR) guidelines were followed to conduct a scoping review of literature on COVID-19 vaccine hesitancy and willingness to vaccinate. Several databases (e.g., MEDLINE, EMBASE, and Google Scholar) were searched to find relevant articles. Intervention- (i.e., COVID-19 vaccine) and outcome- (i.e., hesitancy) related terms were used to search in these databases. The search was conducted on 22 February 2021. Both forward and backward reference lists were checked to find further studies. Three reviewers worked independently to select articles and extract data from selected literature. Studies that used a quantitative survey to measure COVID-19 vaccine hesitancy and acceptance were included in this review. The extracted data were synthesized following the narrative approach and results were represented graphically with appropriate figures and tables. RESULTS: 82 studies were included in this scoping review of 882 identified from our search. Sometimes, several studies had been performed in the same country, and it was observed that vaccine hesitancy was high earlier and decreased over time with the hope of vaccine efficacy. People in different countries had varying percentages of vaccine uptake (28-86.1%), vaccine hesitancy (10-57.8%), vaccine refusal (0-24%). The most common determinants affecting vaccination intention include vaccine efficacy, vaccine side effects, mistrust in healthcare, religious beliefs, and trust in information sources. Additionally, vaccination intentions are influenced by demographic factors such as age, gender, education, and region. CONCLUSIONS: The underlying factors of vaccine hesitancy are complex and context-specific, varying across time and socio-demographic variables. Vaccine hesitancy can also be influenced by other factors such as health inequalities, socioeconomic disadvantages, systemic racism, and level of exposure to misinformation online, with some factors being more dominant in certain countries than others. Therefore, strategies tailored to cultures and socio-psychological factors need to be developed to reduce vaccine hesitancy and aid informed decision-making.