AIMC Topic: Aged

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Unveiling the effect of urinary xenoestrogens on chronic kidney disease in adults: A machine learning model.

Ecotoxicology and environmental safety
Exposure to three primary xenoestrogens (XEs), including phthalates, parabens, and phenols, has been strongly associated with chronic kidney disease (CKD). An interpretable machine learning (ML) model was developed to predict CKD using data from the ...

Machine learning-based prediction of post-induction hypotension: identifying risk factors and enhancing anesthesia management.

BMC medical informatics and decision making
BACKGROUND: Post-induction hypotension (PIH) increases surgical complications including myocardial injury, acute kidney injury, delirium, stroke, prolonged hospitalization, and endangerment of the patient's life. Machine learning is an effective tool...

Machine learning based on nutritional assessment to predict adverse events in older inpatients with possible sarcopenia.

Aging clinical and experimental research
BACKGROUND: The accuracy of current tools for predicting adverse events in older inpatients with possible sarcopenia is still insufficient to develop individualized nutrition-related management strategies. The objectives were to develop a machine lea...

Artificial intelligence assessment of tissue-dissection efficiency in laparoscopic colorectal surgery.

Langenbeck's archives of surgery
PURPOSE: Several surgical-skill assessment tools emphasize the importance of efficient tissue-dissection, whose assessment relies on human judgment and is thus subject to bias. Automated assessment may help solve this problem. This study aimed to ver...

Biophysical versus machine learning models for predicting rectal and skin temperatures in older adults.

Journal of thermal biology
This study compares the efficacy of machine learning models to traditional biophysical models in predicting rectal (T) and skin (T) temperatures of older adults (≥60 years) during prolonged heat exposure. Five machine learning models were trained on ...

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...

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...