The widespread dissemination of misinformation and the diverse public sentiment observed during the COVID-19 pandemic highlight the necessity for accurate sentiment analysis of social media discourse. This study proposes a hybrid deep learning (DL) m...
BACKGROUND: The COVID-19 pandemic highlighted the need to understand factors influencing individuals' risk perceptions and health behaviors. This study aimed to explore the roles of individuals' knowledge, perception, and health-related issues in det...
Chest X-ray (CXR) imaging plays a pivotal role in the diagnosis and prognosis of viral pneumonia. However, distinguishing COVID-19 CXRs from other viral infections remains challenging due to highly similar radiographic features. Most existing deep le...
BACKGROUND: User demographics are often hidden in social media data due to privacy concerns. However, demographic information on substance use (SU) can provide valuable insights, allowing public health policy makers to focus on specific cohorts and d...
Coronavirus disease 2019 (COVID-19), a global pandemic infectious disease, requires early diagnosis and dynamic monitoring to enable timely intervention and reduce the risks of adverse outcomes. To support these needs, we developed an advanced metabo...
BACKGROUND AND OBJECTIVES: Although disease-modifying therapies (DMTs) may suppress coronavirus disease 2019 (COVID-19) vaccine responses in people with multiple sclerosis (pwMS), limited data are available on the cumulative effect of additional boos...
This study aims to enhance the accuracy of pneumonia diagnosis from x-ray images by developing a model that integrates Vision Transformer (ViT) and Graph Convolutional Networks (GCN) for improved feature extraction and diagnostic performance. The ViT...
BMC medical informatics and decision making
Jul 16, 2025
BACKGROUND: This study aims to address the gap in understanding clinicians' attitudes toward explainable AI (XAI) methods applied to machine learning models using tabular data, commonly found in clinical settings. It specifically explores clinicians'...
BACKGROUND: The practice of medicine has evolved significantly during the past decade, with the emergence of Machine Learning (ML) that offers the opportunity of personalized patient-tailored care. However, ML models still face some challenges when c...
In this study, we present a novel approach to analyzing financial crises of the global stock market by leveraging a modified Autoencoder model based on Recurrent Neural Network (RNN-AE). We analyze time series data from 24 global stock markets betwee...
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