Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1093/nutrit/nuae210 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Response to: Problems With Relevance, Certainty, and Misinterpretation of Empirical Evidence.
Nutr Rev
January 2025
FOODiQ Global, Sydney, NSW 2000, Australia.
Problems With Relevance, Certainty, and Misinterpretation of Empirical Evidence in the Umbrella Review "Health Effects of Drinking 100% Juice".
Nutr Rev
January 2025
Food Policy Division, The George Institute for Global Health, The University of New South Wales, Sydney, NSW 2000, Australia.
Health Care Professionals and Data Scientists' Perspectives on a Machine Learning System to Anticipate and Manage the Risk of Decompensation From Patients With Heart Failure: Qualitative Interview Study.
J Med Internet Res
January 2025
NOVA National School of Public Health, Public Health Research Centre, Comprehensive Health Research Center, NOVA University Lisbon, Lisbon, Portugal.
Background: Heart failure (HF) is a significant global health problem, affecting approximately 64.34 million people worldwide. The worsening of HF, also known as HF decompensation, is a major factor behind hospitalizations, contributing to substantial health care costs related to this condition.
View Article and Find Full Text PDFPerformance analysis of image retrieval system using deep learning techniques.
Network
January 2025
Department of Computer Science and Engineering, Knowledge Institute of Technology, Salem, India.
The image retrieval is the process of retrieving the relevant images to the query image with minimal searching time in internet. The problem of the conventional Content-Based Image Retrieval (CBIR) system is that they produce retrieval results for either colour images or grey scale images alone. Moreover, the CBIR system is more complex which consumes more time period for producing the significant retrieval results.
View Article and Find Full Text PDFProductivity Losses due to Health Problems Arising from COVID-19 Pandemic: A Systematic Review of Population-Level Studies Worldwide.
Appl Health Econ Health Policy
January 2025
Department of Health Economics, Nicolaus Copernicus University in Toruń, Bydgoszcz, Poland.
Aim: To systematically review the evidence on productivity losses due to health problems arising from the COVID-19 pandemic based on evidence from population-level studies.
Methods: Following PRISMA statement, we conducted a systematic review using Medline, Embase, Scopus, Web of Science, EconLit, WHO COVID-19 Research and EuropePMC databases and a grey literature search. We included population-level studies using secondary data and qualitatively assessed eligible studies.
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!