AIMC Topic: Cross-Sectional Studies
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Performance Analysis of Hospital Managers Using Fuzzy AHP and Fuzzy TOPSIS: Iranian Experience.
Global journal of health science
Jun 12, 2015
BACKGROUND AND OBJECTIVES: Hospitals are complex organizations that require strong and effective management. The success of such organizations depends on the performance of managers. This study provides a comprehensive set of indicators to assess the...
The Quebec rural emergency department project: a cross-sectional study of a potential two-pronged strategy in the knowledge transfer process.
PloS one
Apr 7, 2015
INTRODUCTION: Health services research generates useful knowledge. Promotion of implementation of this knowledge in medical practice is essential. Prior to initiation of a major study on rural emergency departments (EDs), we deployed two knowledge tr...
Identification of Type 2 Diabetes Risk Factors Using Phenotypes Consisting of Anthropometry and Triglycerides based on Machine Learning.
IEEE journal of biomedical and health informatics
Feb 6, 2015
The hypertriglyceridemic waist (HW) phenotype is strongly associated with type 2 diabetes; however, to date, no study has assessed the predictive power of phenotypes based on individual anthropometric measurements and triglyceride (TG) levels. The ai...
Prediction of hearing loss among the noise-exposed workers in a steel factory using artificial intelligence approach.
International archives of occupational and environmental health
Nov 29, 2014
PURPOSE: Prediction of hearing loss in noisy workplaces is considered to be an important aspect of hearing conservation program. Artificial intelligence, as a new approach, can be used to predict the complex phenomenon such as hearing loss. Using art...
Artificial neural network and falls in community-dwellers: a new approach to identify the risk of recurrent falling?
Journal of the American Medical Directors Association
Oct 29, 2014
BACKGROUND: Identification of the risk of recurrent falls is complex in older adults. The aim of this study was to examine the efficiency of 3 artificial neural networks (ANNs: multilayer perceptron [MLP], modified MLP, and neuroevolution of augmenti...
Identification of the Best Anthropometric Predictors of Serum High- and Low-Density Lipoproteins Using Machine Learning.
IEEE journal of biomedical and health informatics
Aug 20, 2014
Serum high-density lipoprotein (HDL) and low-density lipoprotein (LDL) cholesterol levels are associated with risk factors for various diseases and are related to anthropometric measures. However, controversy remains regarding the best anthropometric...
Utilizing artificial intelligence and medical experts to identify predictors for common diagnoses in dyspneic adults: A cross-sectional study of consecutive emergency department patients from Southern Sweden.
International journal of medical informatics
Oct 1, 2025
OBJECTIVE: Half of all adult emergency department (ED) visits with a complaint of dyspnea involve acute heart failure (AHF), exacerbation of chronic obstructive pulmonary disease (eCOPD), or pneumonia, which are often misdiagnosed. We aimed to create...
Machine learning-based predictive modeling of depressive symptoms in Chinese adolescents.
Journal of affective disorders
Sep 15, 2025
BACKGROUND: The aim is to develop prediction models by lifestyles indicators as well as socioeconomic status to predict the risk of depressive symptoms in adolescents, and to rank and explain these predictors.
When Machines Decide: Exploring How Trust in AI Shapes the Relationship Between Clinical Decision Support Systems and Nurses' Decision Regret: A Cross-Sectional Study.
Nursing in critical care
Sep 1, 2025
BACKGROUND: Artificial intelligence (AI)-based Clinical Decision Support Systems (AI-CDSS) are increasingly implemented in intensive care settings to support nurses in complex, time-sensitive decisions, aiming to improve accuracy, efficiency and pati...
Cardiovascular Indexes and Oxidative Balance in Advanced Cardiovascular-Kidney-Metabolic Syndrome.
Obesity (Silver Spring, Md.)
Sep 1, 2025
OBJECTIVE: The cardiovascular-kidney-metabolic (CKM) syndrome lacks clear strategies for risk reduction.
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