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A deep learning approach for classifying and predicting children's nutritional status in Ethiopia using LSTM-FC neural networks.
BioData Min
January 2025
Department of Statistics, College of Science, Bahir Dar University, P.O. Box 79, Bahir Dar, Ethiopia.
Background: This study employs a LSTM-FC neural networks to address the critical public health issue of child undernutrition in Ethiopia. By employing this method, the study aims classify children's nutritional status and predict transitions between different undernutrition states over time. This analysis is based on longitudinal data extracted from the Young Lives cohort study, which tracked 1,997 Ethiopian children across five survey rounds conducted from 2002 to 2016.
View Article and Find Full Text PDFABCA4 Deep Intronic Variants Contributed to Nearly Half of Unsolved Stargardt Cases With a Milder Phenotype.
Invest Ophthalmol Vis Sci
January 2025
State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, China.
Purpose: The purpose of this study was to investigate the contribution and natural progression of ABCA4 deep intronic variants (DIVs) among a Chinese Stargardt disease (STGD) cohort.
Methods: For unsolved STGD probands, DIVs in ABCA4 were detected by next-generation sequencing, and splicing effects were evaluated by in silico tools and validated through minigene experiments. Comprehensive ocular examinations, especially fundus changes, were carried out and analyzed.
Investigating Smartphone-Based Sensing Features for Depression Severity Prediction: Observation Study.
J Med Internet Res
January 2025
Department of Clinical Psychology and Psychotherapy, Institute of Psychology and Education, Ulm University, Ulm, Germany.
Background: Unobtrusively collected objective sensor data from everyday devices like smartphones provide a novel paradigm to infer mental health symptoms. This process, called smart sensing, allows a fine-grained assessment of various features (eg, time spent at home based on the GPS sensor). Based on its prevalence and impact, depression is a promising target for smart sensing.
View Article and Find Full Text PDFMethodological considerations for estimating indirect costs in children and adolescents with chronic conditions: a scoping review.
BMC Pediatr
January 2025
Chair for Institutional Economics and Health Policy, Department of Philosophy, Politics and Economics, Witten/Herdecke University, Witten, Germany.
Background: In children and adolescents, the prevalence of chronic diseases, e.g., obesity, asthma, and attention-deficit/hyperactivity disorder (ADHD), has increased in the last decades.
View Article and Find Full Text PDFExplore the factors related to the death of offspring under age five and appraise the hazard of child mortality using machine learning techniques in Bangladesh.
BMC Public Health
January 2025
Department of Statistics and Data Science, Jahangirnagar University, Dhaka, 1342, Bangladesh.
Background: Child mortality is a reliable and significant indicator of a nation's health. Although the child mortality rate in Bangladesh is declining over time, it still needs to drop even more in order to meet the Sustainable Development Goals (SDGs). Machine Learning models are one of the best tools for making more accurate and efficient forecasts and gaining in-depth knowledge.
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