AIMC Topic: Middle Aged

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Advanced echocardiography and cluster analysis to identify secondary tricuspid regurgitation phenogroups at different risk.

Revista espanola de cardiologia (English ed.)
INTRODUCTION AND OBJECTIVES: Significant secondary tricuspid regurgitation (STR) is associated with poor prognosis, but its heterogeneity makes predicting patient outcomes challenging. Our objective was to identify STR prognostic phenogroups.

Population norms for the EQ-5D-5L for Hungary: comparison of online surveys and computer assisted personal interviews.

The European journal of health economics : HEPAC : health economics in prevention and care
BACKGROUND AND OBJECTIVES: The aims of this study were to provide population norms for EQ-5D-5L in Hungary and investigate the differences in EQ-5D-5L normative data by survey mode, i.e. online surveys and computer assisted personal interviews (CAPI)...

Visit-to-visit blood pressure variability and clinical outcomes in peritoneal dialysis - based on machine learning algorithms.

Hypertension research : official journal of the Japanese Society of Hypertension
This study aims to investigate the association between visit-to-visit blood pressure variability (VVV) in early stage of continuous ambulatory peritoneal dialysis (CAPD) and long-term clinical outcomes, utilizing machine learning algorithms. Patients...

Machine Learning in Intravascular Ultrasound: Validating Automated Lesion Assessment for Complex Coronary Interventions.

Catheterization and cardiovascular interventions : official journal of the Society for Cardiac Angiography & Interventions
BACKGROUND: Intravascular ultrasound (IVUS) is essential for assessing complex coronary lesions, but remains underutilized in part due to difficulties in image interpretation. The AVVIGO IVUS Automated Lesion Assessment (ALA) software, which uses mac...

Development and validation of short-term, medium-term, and long-term suicide attempt prediction models based on a prospective cohort in Korea.

Asian journal of psychiatry
BACKGROUND: This study aimed to develop and validate prediction models for short-(3 months), medium-(1 year), and long-term suicide attempts among high-risk individuals in South Korea.

Utilizing 12-lead electrocardiogram and machine learning to retrospectively estimate and prospectively predict atrial fibrillation and stroke risk.

Computers in biology and medicine
BACKGROUND: The stroke risk in patients with subclinical atrial fibrillation (AF) is underestimated. By identifying patients at high risk of embolic stroke, health-care professionals can make more informed decisions regarding anticoagulation treatmen...

Explainable paroxysmal atrial fibrillation diagnosis using an artificial intelligence-enabled electrocardiogram.

The Korean journal of internal medicine
BACKGROUND/AIMS: Atrial fibrillation (AF) significantly contributes to global morbidity and mortality. Paroxysmal atrial fibrillation (PAF) is particularly common among patients with cryptogenic strokes or transient ischemic attacks and has a silent ...

Multi-cancer early detection based on serum surface-enhanced Raman spectroscopy with deep learning: a large-scale case-control study.

BMC medicine
BACKGROUND: Early detection of cancer can help patients with more effective treatments and result in better prognosis. Unfortunately, established cancer screening technologies are limited for use, especially for multi-cancer early detection. In this ...

Deep learning models for differentiating three sinonasal malignancies using multi-sequence MRI.

BMC medical imaging
PURPOSE: To develop MRI-based deep learning (DL) models for distinguishing sinonasal squamous cell carcinoma (SCC), adenoid cystic carcinoma (ACC) and olfactory neuroblastoma (ONB) and to evaluate whether the DL models could improve the diagnostic pe...