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Risk Assessment

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The Role of Artificial Intelligence in Obesity Risk Prediction and Management: Approaches, Insights, and Recommendations.

Medicina (Kaunas, Lithuania)
Greater than 650 million individuals worldwide are categorized as obese, which is associated with significant health, economic, and social challenges. Given its overlap with leading comorbidities such as heart disease, innovative solutions are necess...

Artificial Intelligence Applications in Cardio-Oncology: A Comprehensive Review.

Current cardiology reports
PURPOSE OF REVIEW: This review explores the role of artificial intelligence (AI) in cardio-oncology, focusing on its latest application across problems in diagnosis, prognosis, risk stratification, and management of cardiovascular (CV) complications ...

Developing practical machine learning survival models to identify high-risk patients for in-hospital mortality following traumatic brain injury.

Scientific reports
Machine learning (ML) offers precise predictions and could improve patient care, potentially replacing traditional scoring systems. A retrospective study at Emtiaz Hospital analyzed 3,180 traumatic brain injury (TBI) patients. Nineteen variables were...

Impact of pectoral muscle removal on deep-learning-based breast cancer risk prediction.

Physics in medicine and biology
State-of-the-art breast cancer risk (BCR) prediction models have been originally trained on mammograms with pectoral muscle (PM) included. This study investigated whether excluding PM during training/fine-tuning improves the model's BCR discriminatio...

Using artificial intelligence tools for data quality evaluation in the context of microplastic human health risk assessments.

Environment international
Concerns about the negative impacts of microplastics on human health are increasing in society, while exposure and risk assessments require high-quality, reliable data. Although quality assurance and -control (QA/QC) frameworks exist to evaluate the ...

Radiation oncology at crossroads: Rise of AI and managing the unexpected.

Journal of applied clinical medical physics
Integrating artificial intelligence (AI) into radiation oncology has revolutionized clinical workflows, enhancing efficiency, safety, and quality. However, this transformation comes with a price of increased complexity and the emergence of unpredicta...

Machine learning for predicting outcomes of transcatheter aortic valve implantation: A systematic review.

International journal of medical informatics
BACKGROUND: Transcatheter aortic valve implantation (TAVI) therapy has demonstrated its clear benefits such as low invasiveness, to treat aortic stenosis. Despite associated benefits, still post-procedural complications might occur. The severity of t...

Stress hyperglycemia ratio and machine learning model for prediction of all-cause mortality in patients undergoing cardiac surgery.

Cardiovascular diabetology
BACKGROUND: The stress hyperglycemia ratio (SHR) was developed to reduce the effects of long-term chronic glycemic factors on stress hyperglycemia levels, which was associated with adverse clinical outcomes. This study aims to evaluate the relationsh...

Predicting major adverse cardiac events in diabetes and chronic kidney disease: a machine learning study from the Silesia Diabetes-Heart Project.

Cardiovascular diabetology
BACKGROUND: People living with diabetes mellitus (DM) and chronic kidney disease (CKD) are at significantly high risk of cardiovascular events (CVEs), however the predictive performance of traditional risk prediction methods are limited.

A machine learning based death risk analysis and prediction of ST-segment elevation myocardial infarction (STEMI) patients.

Computers in biology and medicine
Acute myocardial infarction is a condition in which a part of the heart muscle cannot receive enough blood due to the narrowing and blockage of the vessels feeding the heart over time. Noticing this situation lately and failing to intervene immediate...