AIMC Topic: Decision Support Techniques

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Predicting high-flow arteriovenous fistulas and cardiac outcomes in hemodialysis patients.

Journal of vascular surgery
BACKGROUND: Heart failure is common in patients receiving hemodialysis. A high-flow arteriovenous fistula (AVF) may represent a modifiable risk factor for heart failure and death. Currently, no tools exist to assess the risk of developing a high-flow...

Fuzzy set-based decision support system for hydrogen sulfide removal technology selection in natural gas processing: a sustainability and efficiency perspective.

Environmental monitoring and assessment
Removing hydrogen sulfide (HS) toxic and corrosive gas from the natural gas processing and utilization industry is a challenging problem for managers of these industries. This problem involves different economic, environmental, and health issues. Var...

A Predictive Model Integrating AI Recognition Technology and Biomarkers for Lung Nodule Assessment.

The Thoracic and cardiovascular surgeon
BACKGROUND:  Lung cancer is the most prevalent and lethal cancer globally, necessitating accurate differentiation between benign and malignant pulmonary nodules to guide treatment decisions. This study aims to develop a predictive model that integrat...

Artificial Intelligence in Diagnosing and Managing Vascular Surgery Patients: An Experimental Study Using the GPT-4 Model.

Annals of vascular surgery
BACKGROUND: The introduction of artificial intelligence (AI) has led to groundbreaking advancements across many scientific fields. Machine learning algorithms have enabled AI models to learn, adapt, and solve complex problems in previously unimaginab...

Predictive Factors Driving Positive Awake Test in Carotid Endarterectomy Using Machine Learning.

Annals of vascular surgery
BACKGROUND: Positive neurologic awake testing during the carotid cross-clamping may be present in around 8% of patients undergoing carotid endarterectomy (CEA). The present work aimed to assess the accuracy of an artificial intelligence (AI)-powered ...

Machine learning for predicting in-hospital mortality in elderly patients with heart failure combined with hypertension: a multicenter retrospective study.

Cardiovascular diabetology
BACKGROUND: Heart failure combined with hypertension is a major contributor for elderly patients (≥ 65 years) to in-hospital mortality. However, there are very few models to predict in-hospital mortality in such elderly patients. We aimed to develop ...

Predicting paediatric asthma exacerbations with machine learning: a systematic review with meta-analysis.

European respiratory review : an official journal of the European Respiratory Society
BACKGROUND: Asthma exacerbations in children pose a significant burden on healthcare systems and families. While traditional risk assessment tools exist, artificial intelligence (AI) offers the potential for enhanced prediction models.