AIMC Topic: Risk Factors

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Predicting the future risk and outcomes of severe heart failure and coronary artery disease with machine learning in the UK Biobank Cohort.

PloS one
BACKGROUND: In order to seriously impact the global burden of heart failure (HF) and coronary artery disease (CAD), identifying at-risk individuals as early as possible is vital. Risk calculator tools in wide clinical use today are informed by tradit...

Machine learning for the prediction of blood transfusion risk during or after mitral valve surgery: a multicenter retrospective cohort study.

Scientific reports
This study aimed to identify the optimal prediction method and key preoperative variables for red blood cell (RBC) transfusion risk in patients undergoing mitral valve surgery. We conducted a retrospective study involving 1477 patients from eight lar...

Interpretable machine learning model predicts 1-year inguinal hernia risk after robot-assisted radical prostatectomy.

Journal of robotic surgery
Inguinal hernia represents a clinically significant yet underreported complication of robot-assisted radical prostatectomy (RARP) for localized prostate cancer, with a notably high incidence within the first postoperative year. Despite its adverse im...

Data-driven identification of key predictors of uncontrolled hypertension: A cross-sectional study.

PloS one
Uncontrolled hypertension (HTN) increases the risk of adverse health events. This study aimed to identify key predictors of uncontrolled HTN in 1,308 Mexican adults with a prior diagnosis of HTN who were undergoing pharmacological treatment. We utili...

From disinfectant to neurodegeneration: Integrating machine learning and mendelian randomization reveals triclosan as a novel environmental risk factor for Alzheimer's disease.

Environmental pollution (Barking, Essex : 1987)
This study systematically investigated the association between triclosan (TCS) exposure and Alzheimer's disease (AD) risk via integrated bioinformatics approaches. TCS-AD-related genes were identified using bioinformatics tools and public databases, ...

AI-driven analysis of diabetes risk determinants in U.S. adults: Exploring disease prevalence and health factors.

PloS one
BACKGROUND: Diabetes remains a major public health concern in the United States, with a complex interplay of behavioral, demographic, and clinical risk factors. This study aims to identify the three best-performing machine learning models for diabete...

Investigating factors influencing fatalities and injuries in animal-vehicle crashes using a random parameters logit model and ensemble machine learning approaches.

PloS one
Animal-vehicle crashes (AVC) pose risks in rural areas, often leading to casualties and injuries. Despite their infrequent occurrence, AVC can have significant consequences, especially when larger animals are involved. This study investigates factors...

Phenotypic Selectivity of Artificial Intelligence-Enhanced Electrocardiography in Cardiovascular Diagnosis and Risk Prediction.

Circulation
BACKGROUND: Artificial intelligence (AI)-enhanced ECG (AI-ECG) models are often designed to detect specific anatomical and functional cardiac abnormalities. Understanding the selectivity of their phenotypic associations is essential to inform their c...

Fibro predict a machine learning risk score for advanced liver fibrosis in the general population using Israeli electronic health records.

Scientific reports
Liver diseases, notably cirrhosis, pose a substantial global health challenge, resulting in millions of annual deaths. Existing diagnostic methods primarily target high-risk groups, leaving a significant portion of patients undiagnosed. This study ai...

Integrating multiple feature assessment methods to identify key predictors of repeat suicide attempts in Taiwan.

BMC psychiatry
BACKGROUND: The high rate of repeat attempts among individuals who have previously attempted suicide presents a critical challenge in public health and suicide prevention. While early and targeted intervention is crucial for this high-risk group, eff...