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Unveiling challenges of maternal health in India's tribal mothers: Insights from a case series of maternal deaths.
J Family Med Prim Care
November 2024
Department of Community Medicine, Netaji Subhash Chandra Bose Medical College, Jabalpur, Madhya Pradesh, India.
Maternal mortality remains a significant public health concern globally, with disparities often evident among marginalized populations, including tribal communities. This case series delves into the complexities surrounding maternal mortality among tribal populations in India, shedding light on the multifaceted factors contributing to this persistent public health issue. Through verbal autopsy and retrospective review of medical records, a series of maternal deaths among tribal mothers were examined.
View Article and Find Full Text PDFMaterial flow analysis (MFA) is used to quantify and understand the life cycles of materials from production to end of use, which enables environmental, social, and economic impacts and interventions. MFA is challenging as available data are often limited and uncertain, leading to an under-determined system with an infinite number of possible stocks and flows values. Bayesian statistics is an effective way to address these challenges by principally incorporating domain knowledge, quantifying uncertainty in the data, and providing probabilities associated with model solutions.
View Article and Find Full Text PDFAutoencoder imputation of missing heterogeneous data for Alzheimer's disease classification.
Healthc Technol Lett
December 2024
Intelligent Systems Research Centre, School of Computing, Engineering and Intelligent Systems Ulster University, Magee campus Derry∼Londonderry Northern Ireland UK.
Missing Alzheimer's disease (AD) data is prevalent and poses significant challenges for AD diagnosis. Previous studies have explored various data imputation approaches on AD data, but the systematic evaluation of deep learning algorithms for imputing heterogeneous and comprehensive AD data is limited. This study investigates the efficacy of denoising autoencoder-based imputation of missing key features of heterogeneous data that comprised tau-PET, MRI, cognitive and functional assessments, genotype, sociodemographic, and medical history.
View Article and Find Full Text PDFDMOIT: denoised multi-omics integration approach based on transformer multi-head self-attention mechanism.
Front Genet
December 2024
Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Republic of Korea.
Multi-omics data integration has become increasingly crucial for a deeper understanding of the complexity of biological systems. However, effectively integrating and analyzing multi-omics data remains challenging due to their heterogeneity and high dimensionality. Existing methods often struggle with noise, redundant features, and the complex interactions between different omics layers, leading to suboptimal performance.
View Article and Find Full Text PDFMixed Matrix Completion in Complex Survey Sampling under Heterogeneous Missingness.
J Comput Graph Stat
March 2024
Department of Statistics, North Carolina State University, Raleigh, NC 27695, USA.
Modern surveys with large sample sizes and growing mixed-type questionnaires require robust and scalable analysis methods. In this work, we consider recovering a mixed dataframe matrix, obtained by complex survey sampling, with entries following different canonical exponential distributions and subject to heterogeneous missingness. To tackle this challenging task, we propose a two-stage procedure: in the first stage, we model the entry-wise missing mechanism by logistic regression, and in the second stage, we complete the target parameter matrix by maximizing a weighted log-likelihood with a low-rank constraint.
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