AIMC Topic: Risk Assessment

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Machine learning-based risk assessment for cardiovascular diseases in patients with chronic lung diseases.

Medicine
The association between chronic lung diseases (CLDs) and the risk of cardiovascular diseases (CVDs) has been extensively recognized. Nevertheless, conventional approaches for CVD risk evaluation cannot fully capture the risk factors (RFs) related to ...

Electrocardiogram-based machine learning for risk stratification of patients with suspected acute coronary syndrome.

European heart journal
BACKGROUND AND AIMS: The importance of risk stratification in patients with chest pain extends beyond diagnosis and immediate treatment. This study sought to evaluate the prognostic value of electrocardiogram feature-based machine learning models to ...

Automated proximal coronary artery calcium identification using artificial intelligence: advancing cardiovascular risk assessment.

European heart journal. Cardiovascular Imaging
AIMS: Identification of proximal coronary artery calcium (CAC) may improve prediction of major adverse cardiac events (MACE) beyond the CAC score, particularly in patients with low CAC burden. We investigated whether the proximal CAC can be detected ...

Artificial intelligence-derived electrocardiographic aging and risk of atrial fibrillation: a multi-national study.

European heart journal
BACKGROUND AND AIMS: Artificial intelligence (AI) algorithms in 12-lead electrocardiogram (ECG) provides promising age prediction methods. This study investigated whether the discrepancy between ECG-derived AI-predicted age (AI-ECG age) and chronolog...

Electrocardiogram-based deep learning to predict mortality in paediatric and adult congenital heart disease.

European heart journal
BACKGROUND AND AIMS: Robust and convenient risk stratification of patients with paediatric and adult congenital heart disease (CHD) is lacking. This study aims to address this gap with an artificial intelligence-enhanced electrocardiogram (ECG) tool ...

Artificial intelligence-enhanced electrocardiography for the identification of a sex-related cardiovascular risk continuum: a retrospective cohort study.

The Lancet. Digital health
BACKGROUND: Females are typically underserved in cardiovascular medicine. The use of sex as a dichotomous variable for risk stratification fails to capture the heterogeneity of risk within each sex. We aimed to develop an artificial intelligence-enha...

Artificial Intelligence: An Emerging Tool for Studying Drug-Induced Liver Injury.

Liver international : official journal of the International Association for the Study of the Liver
Drug-induced liver injury (DILI) is a complex and potentially severe adverse reaction to drugs, herbal products or dietary supplements. DILI can mimic other liver diseases clinical presentation, and currently lacks specific diagnostic biomarkers, whi...

Artificial Intelligence-Enhanced Electrocardiography for Prediction of Incident Hypertension.

JAMA cardiology
IMPORTANCE: Hypertension underpins significant global morbidity and mortality. Early lifestyle intervention and treatment are effective in reducing adverse outcomes. Artificial intelligence-enhanced electrocardiography (AI-ECG) has been shown to iden...

A novel generative multi-task representation learning approach for predicting postoperative complications in cardiac surgery patients.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Early detection of surgical complications allows for timely therapy and proactive risk mitigation. Machine learning (ML) can be leveraged to identify and predict patient risks for postoperative complications. We developed and validated the...

Enhancing patient representation learning with inferred family pedigrees improves disease risk prediction.

Journal of the American Medical Informatics Association : JAMIA
BACKGROUND: Machine learning and deep learning are powerful tools for analyzing electronic health records (EHRs) in healthcare research. Although family health history has been recognized as a major predictor for a wide spectrum of diseases, research...