Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1016/S0140-6736(05)78740-7 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Aligning corporate social responsibility with artificial intelligence in healthcare in the context of the post-COVID-19 recovery: a viewpoint.
J Health Organ Manag
January 2025
University of Malta, Msida, Malta.
Purpose: This study explores how corporate social responsibility (CSR) and artificial intelligence (AI) can be combined in the healthcare industry during the post-COVID-19 recovery phase. The aim is to showcase how this fusion can help tackle healthcare inequalities, enhance accessibility and support long-term sustainability.
Design/methodology/approach: Adopting a viewpoint approach, the study leverages existing literature and case studies to analyze the intersection of CSR and AI.
Evaluation of glomerular filtration rate estimation through plasma clearance of iohexol in seagulls (Larus michahellis).
BMC Vet Res
January 2025
Department of Veterinary Sciences, Veterinary Teaching Hospital "Mario Modenato", University of Pisa, via Livornese snc, San Piero a Grado, Pisa, Italy.
Background: Current endogenous indicators utilised in avian medicine are not sensitive enough to detect renal disease in its early stages. Alternative markers ought to be examined as a result. The aim is to investigate the accuracy of limited-sampling models for glomerular filtration rate (GFR) in adult seagulls using plasma clearance of iohexol (IOX).
View Article and Find Full Text PDFCompassion Competence and Patient Safety Competency in psychiatric nurses.
BMC Nurs
January 2025
Department of Nursing, Faculty of Nursing and Midwifery, Hormozgan University of Medical Sciences, Bandar Abbas, Iran.
Background: Compassion Competence and the ability to strive to understand the suffering of patients in psychiatric ward is essential for nurses to establish effective therapeutic communication in the process of their recovery. Patient Safety Competency is of great importance for nurses to prevent adverse events and minimize errors. This study aimed to investigate the relationship between Compassion Competence and Patient Safety Competency in nurses working in psychiatric wards of Shiraz University of Medical Sciences affiliated hospitals in 2024.
View Article and Find Full Text PDFMachine learning-based estimation of crude oil-nitrogen interfacial tension.
Sci Rep
January 2025
Young Researchers and Elite Club, Omidiyeh Branch, Islamic Azad University, Omidiyeh, Iran.
Accurate estimation of interfacial tension (IFT) between nitrogen and crude oil during nitrogen-based gas injection into oil reservoirs is imperative. The previous research works dealing with prediction of IFT of oil and nitrogen systems consider synthetic oil samples such n-alkanes. In this work, we aim to utilize eight machine learning methods of Decision Tree (DT), AdaBoost (AB), Random Forest (RF), K-nearest Neighbors (KNN), Ensemble Learning (EL), Support Vector Machine (SVM), Convolutional Neural Network (CNN) and Multilayer Perceptron Artificial Neural Network (MLP-ANN) to construct data-driven intelligent models to predict crude oil - nitrogen IFT based upon experimental data of real crude oils samples encountered in underground oil reservoirs.
View Article and Find Full Text PDFEarly prediction of long COVID-19 syndrome persistence at 12 months after hospitalisation: a prospective observational study from Ukraine.
BMJ Open
January 2025
Department of Mathematical Modelling and Artificial Intelligence, National Aerospace University Kharkiv Aviation Institute, Kharkiv, Ukraine.
Objective: To identify the early predictors of a self-reported persistence of long COVID syndrome (LCS) at 12 months after hospitalisation and to propose the prognostic model of its development.
Design: A combined cross-sectional and prospective observational study.
Setting: A tertiary care hospital.
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!