Mining User-generated Content of Mobile Patient Portal: Dimensions of User Experience

Patient portals are positioned as a central component of patient engagement through the potential to change the physician-patient relationship and enable chronic disease self-management. The incorporation of patient portals provides the promise to deliver excellent quality, at optimized costs, while improving the health of the population. This study extends the existing literature by extracting dimensions related to the Mobile Patient Portal Use. We use a topic modeling approach to systematically analyze users’ feedback from the actual use of a common mobile patient portal, Epic\u27s MyChart. Comparing results of Latent Dirichlet Allocation analysis with those of human analysis validated the extracted topics. Practically, the results provide insights into adopting mobile patient portals, revealing opportunities for improvement and to enhance the design of current basic portals. Theoretically, the findings inform the social-technical systems and Task-Technology Fit theories in the healthcare field and emphasize important healthcare structural and social aspects. Further, findings inform the humanization of healthcare framework, support the results of existing studies, and introduce new important design dimensions (i.e., aspects) that influence patient satisfaction and adherence to patient portal

Dark Web Analytics : A Comparative Study of Feature Selection and Prediction Algorithms

The value and size of information exchanged through dark-web pages are remarkable. Recently Many researches showed values and interests in using machine-learning methods to extract security-related useful knowledge from those dark-web pages. In this scope, our goals in this research focus on evaluating best prediction models while analyzing traffic level data coming from the dark web. Results and analysis showed that feature selection played an important role when trying to identify the best models. Sometimes the right combination of features would increase the model’s accuracy. For some feature set and classifier combinations, the Src Port and Dst Port both proved to be important features. When available, they were always selected over most other features. When absent, it resulted in many other features being selected to compensate for the information they provided. The Protocol feature was never selected as a feature, regardless of whether Src Port and Dst Port were available

Enhanced Convolutional Neural Network for Non-Small Cell Lung Cancer Classification

Lung cancer is a common type of cancer that causes death if not detectedearly enough. Doctors use computed tomography (CT) images to diagnoselung cancer. The accuracy of the diagnosis relies highly on the doctor\u27sexpertise. Recently, clinical decision support systems based on deep learningvaluable recommendations to doctors in their diagnoses. In this paper, wepresent several deep learning models to detect non-small cell lung cancer inCT images and differentiate its main subtypes namely adenocarcinoma,large cell carcinoma, and squamous cell carcinoma. We adopted standardconvolutional neural networks (CNN), visual geometry group-16 (VGG16),and VGG19. Besides, we introduce a variant of the CNN that is augmentedwith convolutional block attention modules (CBAM). CBAM aims to extractinformative features by combining cross-channel and spatial information.We also propose variants of VGG16 and VGG19 that utilize a supportvector machine (SVM) at the classification layer instead of SoftMax. Wevalidated all models in this study through extensive experiments on a CTlung cancer dataset. Experimental results show that supplementing CNNwith CBAM leads to consistent improvements over vanilla CNN. Resultsalso show that the VGG variants that use the SVM classifier outperform theoriginal VGGs by a significant margin

Public Discourse Against Masks in the COVID-19 Era: Infodemiology Study of Twitter Data

Background: Despite scientific evidence supporting the importance of wearing masks to curtail the spread of COVID-19, wearing masks has stirred up a significant debate particularly on social media. Objective: This study aimed to investigate the topics associated with the public discourse against wearing masks in the United States. We also studied the relationship between the anti-mask discourse on social media and the number of new COVID-19 cases. Methods: We collected a total of 51,170 English tweets between January 1, 2020, and October 27, 2020, by searching for hashtags against wearing masks. We used machine learning techniques to analyze the data collected. We investigated the relationship between the volume of tweets against mask-wearing and the daily volume of new COVID-19 cases using a Pearson correlation analysis between the two-time series. Results: The results and analysis showed that social media could help identify important insights related to wearing masks. The results of topic mining identified 10 categories or themes of user concerns dominated by (1) constitutional rights and freedom of choice; (2) conspiracy theory, population control, and big pharma; and (3) fake news, fake numbers, and fake pandemic. Altogether, these three categories represent almost 65% of the volume of tweets against wearing masks. The relationship between the volume of tweets against wearing masks and newly reported COVID-19 cases depicted a strong correlation wherein the rise in the volume of negative tweets led the rise in the number of new cases by 9 days. Conclusions: These findings demonstrated the potential of mining social media for understanding the public discourse about public health issues such as wearing masks during the COVID-19 pandemic. The results emphasized the relationship between the discourse on social media and the potential impact on real events such as changing the course of the pandemic. Policy makers are advised to proactively address public perception and work on shaping this perception through raising awareness, debunking negative sentiments, and prioritizing early policy intervention toward the most prevalent topics

Perception of Bias in ChatGPT: Analysis of Social Media Data

In this study, we aim to analyze the public perception of Twitter users with respect to the use of ChatGPT and the potential bias in its responses. Sentiment and emotion analysis were also analyzed. Analysis of 5,962 English tweets showed that Twitter users were concerned about six main types of biases, namely: political, ideological, data & algorithmic, gender, racial, cultural, and confirmation biases. Sentiment analysis showed that most of the users reflected a neutral sentiment, followed by negative and positive sentiment. Emotion analysis mainly reflected anger, disgust, and sadness with respect to bias concerns with ChatGPT use

A Comparative Analysis of Anti-vax Discourse on Twitter Before and After COVID-19 Onset

This study aimed to identify and assess the prevalence of vaccine-hesitancy-related topics on Twitter in the periods before and after the Coronavirus Disease 2019 (COVID-19) outbreak. Using a search query, 272,780 tweets associated with anti-vaccine topics and posted between 1 January 2011, and 15 January 2021, were collected. The tweets were classified into a list of 11 topics and analyzed for trends during the periods before and after the onset of COVID-19. Since the beginning of COVID-19, the percentage of anti-vaccine tweets has increased for two topics, “government and politics” and “conspiracy theories,” and decreased for “developmental disabilities.” Compared to tweets regarding flu and measles, mumps, and rubella vaccines, those concerning COVID-19 vaccines showed larger percentages for the topics of conspiracy theories and alternative treatments, and a lower percentage for developmental disabilities. The results support existing anti-vaccine literature and the assertion that anti-vaccine sentiments are an important public-health issue

Drivers and Challenges of Wearable Devices Use: Content Analysis of Online Users Reviews

With recent advancements in wearable device technologies, there is still a need to investigate drivers and challenges associated with the use of these devices. Following a content analysis approach, this study leverages recent “found large-scale” data to better understand the drivers and challenges that affect the adoption and use of such devices. Analyzing a total of 16,717 online reviews about wearable devices, the findings emphasized the importance of various functionalities (perceived usefulness), appeal, and a number of device design features as the most prominent drivers, while concerns about quality, credibility, and perceived value as potential challenges to wearable adoption and continued use. The findings could inform theoretical models for technology adoption and continued use and can also provide guidance to the design and development of wearable devices

Fake News Detection on Social Media: A Word Embedding-Based Approach

The rapid development of social media, together with the large number of user-generated content on them, has not only connected an unprecedented number of people together to do good stuff, but also has provided convenient platforms to spread misleading pieces of information such as fake news. Existing research has attempted to leverage machine learning to automatically classify fake news. In this paper, we extend such literature by proposing an approach that utilize word embedding and Long Short-Term Memory (LSTM) neural network algorithm. Unlike existing studies, we used two publicly available datasets of news articles to evaluate the proposed model. The results demonstrated the effectiveness of our model against the baseline machine learning models with accuracy of 99% and 96% using the first and second datasets respectively. These comparatively better results and effectiveness compared to existing models demonstrate that pre-trained word embedding models play a significant role in the fake news detection. Keywor

Prediction and Analysis of Bus Adherence to Scheduled Times: San Antonio Transit System

Citizens in large and modern cities heavily rely on smart and efficient public transportation as an alternative to private cars. Public transportation options are expected to be efficient, consistent, and reliable. For example, users of public buses should be able to use their smart phones to reserve and plan their trip at any time. They should also be able to track in real time their routes and any possible delays or issues. Bus adherence to their schedule in public transportation can be modeled as an NP-hard problem. This is due to the many unpredictable factors that can impact such adherence. In this paper, we used deep neural network and regression models to predict bus adherence to scheduled times. We selected San Antonio Transit system as a problem domain and used a dataset containing a snapshot of the adherence of VIA buses from February 2019. We focused on analyzing the significant routes in the dataset and explored the percentage of buses were on time in these routes. Results revealed better performance of neural network models as compared to regression models