AI Medical Compendium Topic:
Risk Assessment

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Machine learning models to predict the bioaccessibility of parent and substituted polycyclic aromatic hydrocarbons (PAHs) in food: Impact on accurate health risk assessment.

Journal of hazardous materials
Food intake is the primary pathway for polycyclic aromatic hydrocarbons (PAHs) to enter the human body. Once ingested, PAHs tend to accumulate, posing health risks. To accurately assess the risk of PAHs from food, concentrations of 10 parent PAHs (PP...

Multi-label material and human risk factors recognition model for construction site safety management.

Journal of safety research
INTRODUCTION: Construction sites are prone to numerous safety risk factors, but safety managers have difficulty managing these risk factors for practical reasons. Moreover, manually identifying multiple risk factors visually is challenging. Therefore...

Modeling health risks using neural network ensembles.

PloS one
This study aims to demonstrate that demographics combined with biometrics can be used to predict obesity related chronic disease risk and produce a health risk score that outperforms body mass index (BMI)-the most commonly used biomarker for obesity....

Development and validation of the PHM-CPA model to predict in-hospital mortality for cirrhotic patients with acute kidney injury.

Digestive and liver disease : official journal of the Italian Society of Gastroenterology and the Italian Association for the Study of the Liver
BACKGROUND: The presence of acute kidney injury (AKI) significantly increases in-hospital mortality risk for cirrhotic patients. Early prognosis prediction for these patients is crucial. We aimed to develop and validate a machine learning model for i...

Machine learning in the prevention of heart failure.

Heart failure reviews
Heart failure (HF) is a global pandemic with a growing prevalence and is a growing burden on the healthcare system. Machine learning (ML) has the potential to revolutionize medicine and can be applied in many different forms to aid in the prevention ...

Clinical Impact of Radiologist's Alert System on Patient Care for High-risk Incidental CT Findings: A Machine Learning-Based Risk Factor Analysis.

Academic radiology
RATIONALE AND OBJECTIVES: Efficient communication between radiologists and clinicians ordering computed tomography (CT) examinations is crucial for managing high-risk incidental CT findings (ICTFs). Herein, we introduced a Radiologist's Alert and Pat...

Enhancing dietary analysis: Using machine learning for food caloric and health risk assessment.

Journal of food science
In the wake of growing concerns regarding diet-related health issues, this study investigates the application of machine learning methods to estimate the energy content and classify the health risks of foods based on the USDA National Nutrient Databa...

Construction and validation of a nomogram prediction model for the catheter-related thrombosis risk of central venous access devices in patients with cancer: a prospective machine learning study.

Journal of thrombosis and thrombolysis
Central venous access devices (CVADs) are integral to cancer treatment. However, catheter-related thrombosis (CRT) poses a considerable risk to patient safety. It interrupts treatment; delays therapy; prolongs hospitalisation; and increases the physi...