AIMC Topic: Risk Assessment

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SHAP-based interpretable machine learning for injury risk prediction in university football players: a multi-dimensional data analysis approach.

Scientific reports
Sports injury prediction is crucial for university football player health, yet existing research predominantly focuses on professional athletes and lacks interpretability. Using the Kaggle "University Football Injury Prediction Dataset" (800 Chinese ...

Prediction of Postoperative Venous Thromboembolism in Patients With Traumatic Brain Injury: Model Development and Validation Study.

JMIR medical informatics
BACKGROUND: Venous thromboembolism (VTE) remains a critical cause of mortality among patients who are hospitalized. Patients with traumatic brain injury (TBI) are particularly susceptible to VTE due to coagulation abnormalities and immobilization. De...

Machine learning-driven geochemical fingerprinting and risk characterization of mineral dust across different operational settings in El-Gedida Iron Mine, Egypt.

Environmental geochemistry and health
Investigating mineral dust emitted from mining activities enables the assessment of environmental risks posed by potentially toxic elements (PTEs) and the discrimination of geochemical fingerprints characteristic of distinct operational settings. Acc...

Genetic factors in the risk assessment of preeclampsia: a review of recent findings.

Molecular biology reports
Preeclampsia, characterized by high blood pressure, proteinuria and organ dysfunction in severe cases is a hypertensive disorder that occurs during pregnancy. There is strong evidence that this disease, whose etiology remains unclear, is a complex co...

Performance of the pediatric index of mortality (PIM-3) in a Moroccan PICU: challenges in resource-limited settings.

European journal of pediatrics
UNLABELLED: Prognostic scores such as the Pediatric Index of Mortality (PIM-3) are widely used to estimate mortality risk in PICUs, yet their performance in low- and middle-income countries (LMICs) remains uncertain. We aimed to evaluate the predicti...

Precision integrated identification of predictive first-trimester metabolomics signatures for early detection of gestational diabetes mellitus.

Cardiovascular diabetology
BACKGROUND AND AIM: Gestational diabetes mellitus (GDM), a common pregnancy-related metabolic disorder, often goes undiagnosed until the second trimester, limiting early intervention opportunities. Given the higher prevalence of GDM in India, there i...

Biogenic amines in fermented foods: A comprehensive review from formation pathways, risk analysis, detection technologies to control measures.

Food research international (Ottawa, Ont.)
Biogenic amines (BAs), typically arising from the fermentation of protein-rich food matrices or products with extended fermentation cycles, posing significant food safety concerns. Excessive intake or long-term accumulation may lead to health hazards...

Genetic and Genomic Testing in Cardiovascular Disease: A Policy Statement From the American Heart Association.

Circulation
The rapid advancement of genomic and precision medicine has expanded the role of genetics and genomics in the diagnosis, risk stratification, and management of cardiovascular diseases. With the decreasing cost and increasing accessibility of genetic ...

Acute myeloid leukemia risk stratification in younger and older patients through transcriptomic machine learning models.

Scientific reports
Acute Myeloid Leukemia (AML) is a genetically and clinically heterogeneous disease that can develop at any age. While AML incidence increases with age and distinct genetic alterations are observed in younger versus older patients, current classificat...