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The Unseen Aftermath: Associations Between the COVID-19 Pandemic and Shifts in Mortality Trends in Japan.
Int J Environ Res Public Health
January 2025
Graduate School of Public Health, St. Luke's International University, Tokyo 104-0045, Japan.
The COVID-19 pandemic disrupted healthcare systems globally, potentially altering mortality trends for non-COVID-19 diseases, particularly in aging populations like Japan's. Assessing these impacts is essential for responsive healthcare planning. We analyzed Japanese vital registration mortality records from January 2018 to December 2021 for adults aged 25 and older, excluding COVID-19-related deaths.
View Article and Find Full Text PDFDual-Stage AI Model for Enhanced CT Imaging: Precision Segmentation of Kidney and Tumors.
Tomography
January 2025
Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA.
Objectives: Accurate kidney and tumor segmentation of computed tomography (CT) scans is vital for diagnosis and treatment, but manual methods are time-consuming and inconsistent, highlighting the value of AI automation. This study develops a fully automated AI model using vision transformers (ViTs) and convolutional neural networks (CNNs) to detect and segment kidneys and kidney tumors in Contrast-Enhanced (CECT) scans, with a focus on improving sensitivity for small, indistinct tumors.
Methods: The segmentation framework employs a ViT-based model for the kidney organ, followed by a 3D UNet model with enhanced connections and attention mechanisms for tumor detection and segmentation.
Hierarchical graph-based integration network for propaganda detection in textual news articles on social media.
Sci Rep
January 2025
EIAS Data Science Lab, College of Computer and Information Sciences, Prince Sultan University, 11586, Riyadh, Saudi Arabia.
During the Covid-19 pandemic, the widespread use of social media platforms has facilitated the dissemination of information, fake news, and propaganda, serving as a vital source of self-reported symptoms related to Covid-19. Existing graph-based models, such as Graph Neural Networks (GNNs), have achieved notable success in Natural Language Processing (NLP). However, utilizing GNN-based models for propaganda detection remains challenging because of the challenges related to mining distinct word interactions and storing nonconsecutive and broad contextual data.
View Article and Find Full Text PDFMomentary Depression Severity Prediction in Patients With Acute Depression Who Undergo Sleep Deprivation Therapy: Speech-Based Machine Learning Approach.
JMIR Ment Health
December 2024
Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim / Heidelberg University, Mannheim, Germany.
Background: Mobile devices for remote monitoring are inevitable tools to support treatment and patient care, especially in recurrent diseases such as major depressive disorder. The aim of this study was to learn if machine learning (ML) models based on longitudinal speech data are helpful in predicting momentary depression severity. Data analyses were based on a dataset including 30 inpatients during an acute depressive episode receiving sleep deprivation therapy in stationary care, an intervention inducing a rapid change in depressive symptoms in a relatively short period of time.
View Article and Find Full Text PDFUnseen Spaces: Psoriasis Inversa Between the Toes.
Br J Dermatol
December 2024
Technical University of Munich, TUM School of Medicine, Department of Dermatology and Allergy, Munich, Germany.
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