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Predicting cognitive decline from neuropsychiatric symptoms and Alzheimer's disease biomarkers: A machine learning approach to a population-based data.
J Alzheimers Dis
January 2025
Department of Neurology and the Franke Barrow Global Neuroscience Education Center, Barrow Neurological Institute, Phoenix, AZ, USA.
Background: The aim of this study was to examine the potential added value of including neuropsychiatric symptoms (NPS) in machine learning (ML) models, along with demographic features and Alzheimer's disease (AD) biomarkers, to predict decline or non-decline in global and domain-specific cognitive scores among community-dwelling older adults.
Objective: To evaluate the impact of adding NPS to AD biomarkers on ML model accuracy in predicting cognitive decline among older adults.
Methods: The study was conducted in the setting of the Mayo Clinic Study of Aging, including participants aged ≥ 50 years with information on demographics (i.
Prediction accuracy of discrete choice experiments in health-related research: a systematic review and meta-analysis.
EClinicalMedicine
January 2025
School of Translational Medicine, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Victoria, Australia.
Background: Discrete choice experiments (DCEs) are increasingly used to inform the design of health products and services. It is essential to understand the extent to which DCEs provide reliable predictions outside of experimental settings in real-world decision-making situations. We aimed to compare the prediction accuracy of stated preferences with real-world choices, as modelled from DCE data.
View Article and Find Full Text PDFChallenges in clinical translation of cardiac magnetic resonance imaging radiomics in non-ischemic cardiomyopathy: a narrative review.
Cardiovasc Diagn Ther
December 2024
The First Affiliated Hospital, Department of Cardiology, Hengyang Medical School, University of South China, Hengyang, China.
Background And Objective: Radiomics is an emerging technology that facilitates the quantitative analysis of multi-modal cardiac magnetic resonance imaging (MRI). This study aims to introduce a standardized workflow for applying radiomics to non-ischemic cardiomyopathies, enabling clinicians to comprehensively understand and implement this technology in clinical practice.
Methods: A computerized literature search (up to August 1, 2024) was conducted using PubMed to identify relevant studies on the roles and workflows of radiomics in non-ischemic cardiomyopathy.
Evaluation of myocardial perfusion imaging techniques and artificial intelligence (AI) tools in coronary artery disease (CAD) diagnosis through multi-criteria decision-making method.
Cardiovasc Diagn Ther
December 2024
Operational Research Center in Healthcare, Near East University, Nicosia, Turkey.
Background: Cardiovascular diseases (CVDs) continue to be the world's greatest cause of death. To evaluate heart function and diagnose coronary artery disease (CAD), myocardial perfusion imaging (MPI) has become essential. Artificial intelligence (AI) methods have been incorporated into diagnostic methods such as MPI to improve patient outcomes in recent years.
View Article and Find Full Text PDFEffects of Long-Term Storage on the Isotopic Compositions of Different Types of Environmental Waters.
Rapid Commun Mass Spectrom
April 2025
Department of Earth and Environmental Sciences, Indiana University Indianapolis, Indianapolis, Indiana, USA.
Rationale: Fog, dew, and rain are crucial for sustaining ecosystem functions, especially in water-limited regions. However, they are subject to isotopic changes during storage due to their usual small sample volumes and inherent sensitivity to atmospheric particulates. Understanding long-term storage effects on these water samples is essential for ensuring isotopic integrity.
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