Acute myocardial infarction (AMI) substantially fuels the worldwide escalation in both morbidity and mortality. The hemoglobin glycation index (HGI) is linked to a range of undesirable outcomes, but its relationship with short-term outcomes in AMI pa...
Survival analysis plays a crucial aspect in medical research and other domains where understanding the time-to-events is paramount. In this study, we present a novel approach for estimating survival outcomes that combines Bayesian neural networks wit...
Glioblastoma is an aggressive, malignant primary brain tumour and the most prevalent histological type of glioma. Our study attempted to investigate the independent predictors of overall survival (OS) and cancer-specific survival (CSS) in Asian patie...
This study explores the relationships between biochemical phenotypes identified using machine learning, and key health outcomes, including body composition, physical function, and mortality risk. Data were collected from 536 physically active Spanish...
BACKGROUND: Interstitial lung disease (ILD) prognostication incorporates clinical history, pulmonary function testing (PFTs), and chest CT pattern classifications. The machine learning classifier, Fibresolve, includes a model to help detect CT patter...
PURPOSE: To develop and validate a hybrid radiomics model to predict the overall survival in pancreatic cancer patients and identify risk factors that affect patient prognosis.
BACKGROUND: Delirium frequently occurs as a severe complication among patients with acute pancreatitis (AP), contributing to extended hospital stays, higher mortality rates, and lasting cognitive deficits. The pathogenesis of delirium in this setting...
Alzheimer's disease (AD), a progressive neurodegenerative disorder, significantly impacts patient survival, prompting the need for accurate prognostic tools. Lifestyle factors and physical activity levels have been identified as critical modifiable r...
Understanding how risk factors interact to jointly influence disease risk can provide insights into disease development and improve risk prediction. Here we introduce survivalFM, a machine learning extension to the widely used Cox proportional hazard...
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