AIMC Topic: Middle East

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Anemia or other comorbidities? using machine learning to reveal deeper insights into the drivers of acute coronary syndromes in hospital admitted patients.

PloS one Jan 24, 2022
Acute coronary syndromes (ACS) are a leading cause of deaths worldwide, yet the diagnosis and treatment of this group of diseases represent a significant challenge for clinicians. The epidemiology of ACS is extremely complex and the relationship betw...
Humans Machine Learning Male Middle Aged Female Aged Models, Cardiovascular Middle East Comorbidity Acute Coronary Syndrome Registries Hospital Mortality Anemia Patient Admission Risk Assessment
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Sensors Around the World: Europe, Middle East, and Africa.

ACS sensors Dec 16, 2021
Middle East Neural Networks, Computer Africa Europe
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Identifying β-thalassemia carriers using a data mining approach: The case of the Gaza Strip, Palestine.

Artificial intelligence in medicine May 3, 2018
Thalassemia is considered one of the most common genetic blood disorders that has received excessive attention in the medical research fields worldwide. Under this context, one of the greatest challenges for healthcare professionals is to correctly d...
Genetic Carrier Screening Heterozygote Middle East Asymptomatic Diseases Neural Networks, Computer Phenotype Databases, Factual Biomarkers Humans Hemoglobins Data Mining Erythrocyte Indices beta-Thalassemia Reproducibility of Results Predictive Value of Tests Decision Trees Bayes Theorem
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AI usage among medical students in Palestine: a cross-sectional study and demonstration of AI-assisted research workflows.

BMC medical education May 12, 2025
BACKGROUND: Artificial Intelligence (AI) is transforming medical education globally, offering solutions to challenges such as resource limitations and limited clinical exposure. However, its integration in resource-constrained settings like Palestine...
Academic Performance Middle East Young Adult Adult Students, Medical Male Artificial Intelligence Humans Biomedical Research Cross-Sectional Studies Surveys and Questionnaires Female Arabs Clinical Competence Education, Medical, Undergraduate Workflow
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Global diabetes burden: analysis of regional differences to improve diabetes care.

BMJ open diabetes research & care Oct 1, 2022
INTRODUCTION: The current evaluation processes of the burden of diabetes are incomplete and subject to bias. This study aimed to identify regional differences in the diabetes burden on a universal level from the perspective of people with diabetes.
Humans Machine Learning Middle East North America Diabetes Mellitus Europe
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