AIMC Topic: Risk Factors

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Explore the factors related to the death of offspring under age five and appraise the hazard of child mortality using machine learning techniques in Bangladesh.

BMC public health
BACKGROUND: Child mortality is a reliable and significant indicator of a nation's health. Although the child mortality rate in Bangladesh is declining over time, it still needs to drop even more in order to meet the Sustainable Development Goals (SDG...

A deep learning analysis for dual healthcare system users and risk of opioid use disorder.

Scientific reports
The opioid crisis has disproportionately affected U.S. veterans, leading the Veterans Health Administration to implement opioid prescribing guidelines. Veterans who receive care from both VA and non-VA providers-known as dual-system users-have an inc...

Machine learning to predict stroke risk from routine hospital data: A systematic review.

International journal of medical informatics
PURPOSE: Stroke remains a leading cause of morbidity and mortality. Despite this, current risk stratification tools such as CHADS-VASc and QRISK3 are of limited accuracy, particularly in those without a diagnosis of atrial-fibrillation. Hence, there ...

Development and validation of machine learning-based prediction model for central venous access device-related thrombosis in children.

Thrombosis research
BACKGROUND: Identifying independent risk factors and implementing high-quality assessment tools for early detection of patients at high risk of central venous access device (CVAD)-related thrombosis (CRT) plays a critical role in delivering timely pr...

Antiviral therapy can effectively suppress irAEs in HBV positive hepatocellular carcinoma treated with ICIs: validation based on multi machine learning.

Frontiers in immunology
BACKGROUND: Immune checkpoint inhibitors have proven efficacy against hepatitis B-virus positive hepatocellular. However, Immunotherapy-related adverse reactions are still a major challenge faced by tumor immunotherapy, so it is urgent to establish n...

Predicting bullying victimization among adolescents using the risk and protective factor framework: a large-scale machine learning approach.

BMC public health
BACKGROUND: Bullying, encompassing physical, psychological, social, or educational harm, affects approximately 1 in 20 United States teens aged 12-18. The prevalence and impact of bullying, including online bullying, necessitate a deeper understandin...

Machine Learning-Based predictive model for adolescent metabolic syndrome: Utilizing data from NHANES 2007-2016.

Scientific reports
Metabolic syndrome (Mets) in adolescents is a growing public health issue linked to obesity, hypertension, and insulin resistance, increasing risks of cardiovascular disease and mental health problems. Early detection and intervention are crucial but...

A comprehensive analysis of stroke risk factors and development of a predictive model using machine learning approaches.

Molecular genetics and genomics : MGG
Stroke is a leading cause of death and disability globally, particularly in China. Identifying risk factors for stroke at an early stage is critical to improving patient outcomes and reducing the overall disease burden. However, the complexity of str...