AIMC Topic: Risk Factors

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The additive effect of the estimated glucose disposal rate and a body shape index on cardiovascular disease: A cross-sectional study.

PloS one
BACKGROUND: The glucose disposal rate (eGDR) and a body shape index (ABSI) are predictors strongly associated with cardiovascular disease (CVD) and outcomes. However, whether they have additive effects on CVD risk is unknown. This study aimed to inve...

Advancing fall risk prediction in older adults with cognitive frailty: A machine learning approach using 2-year clinical data.

PloS one
Falls are a critical concern in older adults with cognitive frailty (CF). However, previous studies have not fully examined whether machine learning models can predict falls in older individuals with CF. The 2-year longitudinal data set from the Kore...

Entropy-based risk network identification in adolescent self-injurious behavior using machine learning and network analysis.

Translational psychiatry
Adolescent Self-Injurious Behavior (SIB) is a significant global public health issue, with a lifetime prevalence rate of approximately 13.7%. As awareness of SIB rises, there is an urgent need for effective prediction mechanisms to enable early ident...

Machine learning-based prediction model for post-stroke cerebral-cardiac syndrome: a risk stratification study.

Scientific reports
Cerebral-cardiac syndrome (CCS) is a severe cardiac complication following acute ischemic stroke, often associated with adverse outcomes. This study developed and validated a machine learning (ML) model to predict CCS using clinical, laboratory, and ...

Predicting post-liver transplantation mortality: a retrospective cohort study on risk factor identification and prognostic nomogram construction.

European journal of medical research
BACKGROUND: To identify risk factors for post-transplant mortality and develop a machine learning-integrated prognostic tool to optimise clinical decision-making in liver transplantation (LT) recipients.

Predicting in-hospital mortality in ICU patients with lymphoma using machine learning models.

PloS one
BACKGROUND: Lymphoma is a severe condition with high mortality rates, often requiring ICU admission. Traditional risk stratification tools like SOFA and APACHE scores struggle to capture complex clinical interactions. Machine learning (ML) models off...

Predictive value of anthropometric indices for incident of dyslipidemia: a large population-based study.

Population health metrics
INTRODUCTION: Dyslipidemia as a modifiable risk factor for chronic non-communicable diseases has become a worldwide concern. We aim to explore different anthropometric measures as predictors of dyslipidemia using various machine learning methods.

Predicting cancer risk using machine learning on lifestyle and genetic data.

Scientific reports
Cancer remains one of the leading causes of mortality worldwide, where early detection significantly improves patient outcomes and reduces treatment burden. This study investigates the application of Machine Learning (ML) techniques to predict cancer...

Risk factors for tuberculosis treatment outcomes: a statistical learning-based exploration using the SINAN database with incomplete observations.

BMC medical informatics and decision making
BACKGROUND: Understanding early predictors of treatment outcomes allows better outcome prediction and resource allocation for efficient tuberculosis (TB) management.

Development and validation of a machine learning model for predicting vulnerable carotid plaques using routine blood biomarkers and derived indicators: insights into sex-related risk patterns.

Cardiovascular diabetology
BACKGROUND: Early detection of vulnerable carotid plaques is critical for stroke prevention. This study aimed to develop a machine learning model based on routine blood tests and derived indices to predict plaque vulnerability and assess sex-specific...