Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet

Background: Stroke, a cerebrovascular disease, is one of the major causes of death. It causes significant health and financial burdens for both patients and health care systems. One of the important risk factors for stroke is health-related behavior, which is becoming an increasingly important focus of prevention. Many machine learning models have been built to predict the risk of stroke or to automatically diagnose stroke, using predictors such as lifestyle factors or radiological imaging. However, there have been no models built using data from lab tests. Objective: The aim of this study was to apply computational methods using machine learning techniques to predict stroke from lab test data. Methods: We used the National Health and Nutrition Examination Survey data sets with three different data selection methods (ie, without data resampling, with data imputation, and with data resampling) to develop predictive models. We used four machine learning classifiers and six performance measures to evaluate the performance of the models. Results: We found that accurate and sensitive machine learning models can be created to predict stroke from lab test data. Our results show that the data resampling approach performed the best compared to the other two data selection techniques. Prediction with the random forest algorithm, which was the best algorithm tested, achieved an accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and area under the curve of 0.96, 0.97, 0.96, 0.75, 0.99, and 0.97, respectively, when all of the attributes were used. Conclusions: The predictive model, built using data from lab tests, was easy to use and had high accuracy. In future studies, we aim to use data that reflect different types of stroke and to explore the data to build a prediction model for each type.

A healthy pregnancy is critical to ensure a healthy birth. Uncertainty and a lack of information often lead to anxiety during pregnancy, which may negatively affect health outcomes. Women become inquisitive during this period and increasingly seek out pregnancy-related health information online. In this study, we aim to understand the unmet information needs of obstetric ultrasound exams through a qualitative content analysis of social media posts. This is a qualitative content analysis study. We systematically gathered pregnancy-related consumer health questions from three public forums (Yahoo! Answers, BabyCenter, PregnancyForum) and one ultrasound internet forum (Reddit/ultrasound). We used a purposive sampling strategy. We only included questions about ultrasound exams, and we excluded any questions related the cost or training of the ultrasound exam. We also coded the emotion expressed by the women in the questions we included. After coding each question, we categorized the data into thematic topics and subtopics. We collected a total of 435 questions that met the inclusion criteria. The thematic analysis included nine major themes. These themes include ultrasound exam preparation, exam expectancy, timing of the ultrasound exam, image interpretation, report interpretation, result explanation, women’s emotions, and healthy lifestyle. In addition, we identified seventeenth subtopics. In this social media study, we discovered major information gaps that should be filled to better support health consumers’ understanding of obstetric ultrasound exams and result reports. The information gaps could become barriers to healthy decision-making and create stress among pregnant women. More work needs to be done to mitigate the information gaps, such as providing more consumer-friendly and personalized information during pregnancy.

BACKGROUND Stroke, a cerebrovascular disease, is one of the major causes of death. It causes significant health and financial burdens for both patients and health care systems. One of the important risk factors for stroke is health-related behavior, which is becoming an increasingly important focus of prevention. Many machine learning models have been built to predict the risk of stroke or to automatically diagnose stroke, using predictors such as lifestyle factors or radiological imaging. However, there have been no models built using data from lab tests. OBJECTIVE The aim of this study was to apply computational methods using machine learning techniques to predict stroke from lab test data. METHODS We used the National Health and Nutrition Examination Survey data sets with three different data selection methods (ie, without data resampling, with data imputation, and with data resampling) to develop predictive models. We used four machine learning classifiers and six performance measures to evaluate the performance of the models. RESULTS We found that accurate and sensitive machine learning models can be created to predict stroke from lab test data. Our results show that the data resampling approach performed the best compared to the other two data selection techniques. Prediction with the random forest algorithm, which was the best algorithm tested, achieved an accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and area under the curve of 0.96, 0.97, 0.96, 0.75, 0.99, and 0.97, respectively, when all of the attributes were used. CONCLUSIONS The predictive model, built using data from lab tests, was easy to use and had high accuracy. In future studies, we aim to use data that reflect different types of stroke and to explore the data to build a prediction model for each type.

Background: Emerging research has highlighted the potential of virtual reality (VR) as a tool for training health care students and professionals in care skills for individuals with Alzheimer disease and related dementias (ADRD). However, there is limited research on the use of VR to engage the general public in raising awareness about ADRD. Objective: This research aimed to examine the impact of the VR video “A Walk-Through Dementia” on YouTube users by analyzing their posts. Methods: We collected 12,754 comments from the VR video series “A Walk-Through Dementia,” which simulates the everyday challenges faced by individuals with ADRD, providing viewers with an immersive experience of the condition. Topic modeling was conducted to gauge viewer opinions and reactions to the videos. A pretrained Bidirectional Encoder Representations from Transformers (BERT) model was used to transform the YouTube comments into high-dimensional vector embeddings, allowing for systematic identification and detailed analysis of the principal topics and their thematic structures within the dataset. Results: We identified the top 300 most frequent words in the dataset and categorized them into nouns, verbs, and adjectives or adverbs using a part-of-speech tagging model, fine-tuned for accurate tagging tasks. The topic modeling process identified eight8 initial topics based on the most frequent words. After manually reviewing the 8 topics and the content of the comments, we synthesized them into 5 themes. The predominant theme, represented in 2917 comments, centered on users’ personal experiences with the impact of ADRD on patients and caregivers. The remaining themes were categorized into 4 main areas: positive reactions to the VR videos, challenges faced by individuals with ADRD, the role of caregivers, and learning from the VR videos. Conclusions: Using topic modeling, this study demonstrated that VR applications serve as engaging and experiential learning tools, offering the public a deeper understanding of life with ADRD. Future research should explore additional VR applications on social media, as they hold the potential to reach wider audiences and effectively disseminate knowledge about ADRD.

Alfouzan RA, et al. J Glob Health Sci. 2025 Dec;7:e25. https://doi.org/10.35500/jghs.2025.7.e25