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Information found in the social media may help to set up infoveillance and track epidemics, identify high-risk behaviours, or assess trends or feelings about a subject or event. We developed a dashboard to enable novice users to easily and autonomously extract and analyze data from Twitter. Eleven users tested the dashboard and considered the tool to be highly usable and useful. They were able to conduct the research they wanted and appreciated being able to use this tool without having to program.
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http://dx.doi.org/10.3233/SHTI220562 | DOI Listing |
Brain Behav
September 2025
Department of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran.
Purpose: Depression among college students is a growing concern that negatively affects academic performance, emotional well-being, and career planning. Existing diagnostic methods are often slow, subjective, and inaccessible, underscoring the need for automated systems that can detect depressive symptoms through digital behavior, particularly on social media platforms.
Method: This study proposes a novel natural language processing (NLP) framework that combines a RoBERTa-based Transformer with gated recurrent unit (GRU) layers and multimodal embeddings.
J Cardiovasc Magn Reson
September 2025
Royal Brompton and Harefield Hospitals, part of Guy's and St Thomas' NHS Foundation Trust, London, UK; National Heart and Lung Institute, Imperial College London, UK. Electronic address:
Background: Serial perfusion cardiovascular magnetic resonance (CMR) in symptomatic patients undergoing coronary artery bypass grafting (CABG) may provide mechanistic insight into dynamic abnormalities of the myocardium.
Objectives: To assess how changes in cardiac reperfusion and remodelling associate with symptom improvement in patients undergoing CABG METHODS: Patients awaiting elective CABG completed serial quality of life questionnaires and detailed CMR at baseline and at 6-12 months post CABG as per protocol. Automated fully quantitative stress and rest myocardial blood flow was calculated, alongside assessment of the visual ischaemic burden.
J Cardiovasc Magn Reson
August 2025
Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.
Background: Late Gadolinium Enhancement (LGE) imaging remains the gold standard for assessing myocardial fibrosis and scarring, with left ventricular (LV) LGE presence and extent serving as a predictor of major adverse cardiac events (MACE). Despite its clinical significance, LGE-based LV scar quantification is not used routinely due to the labor-intensive manual segmentation and substantial inter-observer variability.
Methods: We developed ScarNet that synergistically combines a transformer-based encoder in Medical Segment Anything Model (MedSAM), which we fine-tuned with our dataset, and a convolution-based decoder in U-Net with tailored attention blocks to automatically segment myocardial scar boundaries while maintaining anatomical context.
J Med Internet Res
August 2025
Department of Biomedical Informatics, Vanderbilt University Medical Center, 2525 West End Avenue, Nashville, TN, 37203, United States, 1 2139151696.
Background: Mortality is a critical variable in health care research, especially for evaluating medical product safety and effectiveness. However, inconsistencies in the availability and timeliness of death date and cause of death (CoD) information present significant challenges. Conventional sources such as the National Death Index and electronic health records often experience data lags, missing fields, or incomplete coverage, limiting their utility in time-sensitive or large-scale studies.
View Article and Find Full Text PDFBreast Cancer Res Treat
August 2025
Department of Surgery, Section of Plastic Surgery, University of Michigan, Ann Arbor, MI, USA.
Purpose: Breast cancer remains a global public health burden. This study aimed to evaluate the readability of breast cancer articles shared on X (formerly Twitter) during Breast Cancer Awareness Month (October 2024), and it explores the possibility of using artificial intelligence (AI) to improve readability.
Methods: We identified the top articles (n = 377) from posts containing #breastcancer on X during October 2024.