AIMC Topic: Risk Factors

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Machine learning in predicting preoperative intra-aortic balloon pump use in patients undergoing coronary artery bypass grafting.

Journal of cardiothoracic surgery
BACKGROUND: Intra-aortic balloon pump (IABP) implantation in the perioperative period of cardiac surgery is an auxiliary treatment for cardiogenic shock. However, there is a lack of effective prediction models for preoperative IABP implantation.

A comparative analysis of heterogeneity in lung cancer screening effectiveness in two randomised controlled trials.

Nature communications
Clinical trials demonstrate that screening can reduce lung cancer mortality by over 20%. However, lung cancer screening effectiveness (reduction in lung cancer specific mortality) may vary by personal risk-factors. Here we evaluate heterogeneity in l...

Hospital acquired drug resistant pathogens infections in patients with viral respiratory tract infections: a retrospective study.

BMC infectious diseases
BACKGROUND: Viral respiratory infections (VRTIs) caused by influenza (Flu) and COVID-19 pose significant global health challenges. Clinical outcomes are further exacerbated by infections with hospital acquired drug resistant pathogens (DRPs).

Constructing a predictive model for acute mastitis in lactating women based on machine learning.

Scientific reports
Acute lactational mastitis is a frequently occurring complication for lactating women, exerting a certain degree of influence on their physical condition, breastfeeding, mental health, and daily life. The etiology of this disease is complex, and the ...

Machine-learning approach to atrial fibrillation prediction among individuals without prior cardiovascular diseases.

Open heart
BACKGROUND: There is a lack of atrial fibrillation (AF) prediction models tailored for individuals without prior cardiovascular diseases (CVDs) to facilitate early intervention. This study aimed to develop and validate an AF prediction model using ma...

Predictive model integrating deep learning and clinical features based on ultrasound imaging data for surgical intervention in intussusception in children younger than 8 months.

BMJ open
OBJECTIVES: The objective of this study was to identify risk factors for enema reduction failure and to establish a combined model that integrates deep learning (DL) features and clinical features for predicting surgical intervention in intussuscepti...

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...