AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Risk Factors

Showing 1 to 10 of 2299 articles

Clear Filters

A systematic review and meta-analysis of lung cancer risk prediction models.

Acta oncologica (Stockholm, Sweden)
BACKGROUND: Lung cancer (LC) remains the leading cause of cancer-related mortality worldwide. Early detection through targeted screening significantly improves patient outcomes. However, identifying high-risk individuals remains a critical challenge.

Interpretable artificial intelligence model for predicting heart failure severity after acute myocardial infarction.

BMC cardiovascular disorders
BACKGROUND: Heart failure (HF) after acute myocardial infarction (AMI) is a leading cause of mortality and morbidity worldwide. Accurate prediction and early identification of HF severity are crucial for initiating preventive measures and optimizing ...

Application of interpretable machine learning algorithms to predict macroangiopathy risk in Chinese patients with type 2 diabetes mellitus.

Scientific reports
Macrovascular complications are leading causes of morbidity and mortality in patients with type 2 diabetes mellitus (T2DM), yet early diagnosis of cardiovascular disease (CVD) in this population remains clinically challenging. This study aims to deve...

Machine Learning Model for Predicting Coronary Heart Disease Risk: Development and Validation Using Insights From a Japanese Population-Based Study.

JMIR cardio
BACKGROUND: Coronary heart disease (CHD) is a major cause of morbidity and mortality worldwide. Identifying key risk factors is essential for effective risk assessment and prevention. A data-driven approach using machine learning (ML) offers advanced...

All-Cause Mortality Risk in Elderly Patients with Femoral Neck and Intertrochanteric Fractures: A Predictive Model Based on Machine Learning.

Clinical interventions in aging
INTRODUCTION: The aim of this study was to identify the influencing factors for all-cause mortality in elderly patients with intertrochanteric and femoral neck fractures and to construct predictive models.

Development and validation of a novel predictive model for dementia risk in middle-aged and elderly depression individuals: a large and longitudinal machine learning cohort study.

Alzheimer's research & therapy
BACKGROUND: Depression serves as a prodromal symptom of dementia, and individuals with depression exhibit a significantly higher risk of developing dementia. The aim of this study is to develop and validate a novel dementia risk prediction tool among...

Leveraging explainable artificial intelligence with ensemble of deep learning model for dementia prediction to enhance clinical decision support systems.

Scientific reports
The prevalence of dementia is growing worldwide due to the fast ageing of the population. Dementia is an intricate illness that is frequently produced by a mixture of genetic and environmental risk factors. There is no treatment for dementia yet; the...

Machine learning for predicting all-cause mortality of metabolic dysfunction-associated fatty liver disease: a longitudinal study based on NHANES.

BMC gastroenterology
BACKGROUND: The mortality burden of metabolic dysfunction-associated fatty liver disease (MAFLD) is rising, making it crucial to predict mortality and identify the factors influencing it. While advanced machine learning algorithms are gaining recogni...

Comparing interpretable machine learning models for fall risk in middle-aged and older adults with and without pain.

Scientific reports
Pain is common in middle-aged and older adults, has also been identified as a fall risk factor, whereas the mechanism of falls in pain is unclear. This study included 13,074 middle-aged and older adults from the China health and retirement longitudin...