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Inverse Gini indexed averaging: A multi-leveled ensemble approach for skin lesion classification using attention-integrated customized ResNet variants.
Digit Health
January 2025
Civil Engineering Department, Daffodil International University, Dhaka, Bangladesh.
Objective: To improve the accuracy and explainability of skin lesion detection and classification, particularly for several types of skin cancers, through a novel approach based on the convolutional neural networks with attention-integrated customized ResNet variants (CRVs) and an optimized ensemble learning (EL) strategy.
Methods: Our approach utilizes all ResNet variants combined with three attention mechanisms: channel attention, soft attention, and squeeze-excitation attention. These attention-integrated ResNet variants are aggregated through a unique multi-level EL strategy.
Racial residential segregation is associated with ambient air pollution exposure after adjustment for multilevel sociodemographic factors: Evidence from eight US-based cohorts.
Environ Epidemiol
February 2025
Department of Epidemiology, University of Washington School of Public Health, Seattle, Washington.
Objective: We examined if racial residential segregation (RRS) - a fundamental cause of disease - is independently associated with air pollution after accounting for other neighborhood and individual-level sociodemographic factors, to better understand its potential role as a confounder of air pollution-health studies.
Methods: We compiled data from eight large cohorts, restricting to non-Hispanic Black and White urban-residing participants observed at least once between 1999 and 2005. We used 2000 decennial census data to derive a spatial RRS measure (divergence index) and neighborhood socioeconomic status (NSES) index for participants' residing Census tracts, in addition to participant baseline data, to examine associations between RRS and sociodemographic factors (NSES, education, race) and residential exposure to spatiotemporal model-predicted PM and NO levels.
Efficient and accurate variational inference for multilevel threshold autoregressive models in intensive longitudinal data.
Br J Math Stat Psychol
January 2025
Department of Community Health Sciences, University of Manitoba, Winnipeg, Manitoba, Canada.
Recent technological advancements have enabled the collection of intensive longitudinal data (ILD), consisting of repeated measurements from the same individual. The threshold autoregressive (TAR) model is often used to capture the dynamic outcome process in ILD, with autoregressive parameters varying based on outcome variable levels. For ILD from multiple individuals, multilevel TAR (ML-TAR) models have been proposed, with Bayesian approaches typically used for parameter estimation.
View Article and Find Full Text PDFAssessing the Impact of Non-Pharmaceutical Interventions During the COVID-19 Pandemic on RSV Seasonality in Europe.
Influenza Other Respir Viruses
January 2025
Nivel - Netherlands Institute for Health Services Research, Utrecht, The Netherlands.
Background: During the COVID-19 pandemic, atypical respiratory syncytial virus (RSV) circulation patterns emerged, with the occurrence of RSV activity outside the typical winter season. This study investigates the impact of COVID-19 and associated non-pharmaceutical interventions (NPIs) on RSV seasonality.
Methods: The onset, offset and peak of RSV epidemics from 2018 to 2022 across 12 European countries were determined using the 3% positivity threshold method.
Beyond LDL cholesterol: remnant cholesterol is associated with cardiometabolic risk factors in children.
BMC Med
January 2025
Health and Social Research Center, Universidad de Castilla-La Mancha, Cuenca, Spain.
Background: Recent evidence from both randomized controlled trials and cohort studies in adults suggests that plasma remnant cholesterol (RC) levels predict cardiovascular disease. In children, studies are scarce, although high levels of RC might represent a marker of early atherosclerotic damage. Thus, the aim of this study was to explore the cardiometabolic risk associated with RC, which extends beyond low-density lipoprotein cholesterol (LDL-c) in children.
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