AIMC Topic: Female

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Automated deep U-Net model for ischemic stroke lesion segmentation in the sub-acute phase.

Scientific reports
Manual segmentation of sub-acute ischemic stroke lesions in fluid-attenuated inversion recovery magnetic resonance imaging (FLAIR MRI) is time-consuming and subject to inter-observer variability, limiting clinical workflow efficiency. To develop and ...

Neurometabolic predictors of mental effort in the frontal cortex.

Translational psychiatry
Motivation drives individuals to overcome costs to achieve desired outcomes, such as rewards or avoidance of punishment, with significant variability across individuals. The dorsomedial prefrontal cortex/dorsal anterior cingulate cortex (dmPFC/dACC) ...

Predicting risk of early-onset sepsis in low-resource neonatal units using routine healthcare data: development and evaluation of multivariable statistical and machine learning models.

BMJ paediatrics open
BACKGROUND: Neonatal sepsis is a major cause of morbidity and mortality in low-resource settings and accurate, context-appropriate diagnostic methods are urgently needed to improve clinical outcomes.

Transformer-based AI approach to unravel long-term, time-dependent prognostic complexity in patients with advanced NSCLC and PD-L1 ≥50%: insights from the pembrolizumab 5-year global registry.

Journal for immunotherapy of cancer
BACKGROUND: With nearly one-third of patients with advanced non-small cell lung cancer (NSCLC) and PD-L1 Tumor Proportion Score≥50% surviving beyond 5 years following first-line pembrolizumab, long-term outcomes challenge traditional paradigms of can...

Preferences, Perceptions, and Use of Online Nutrition Content Among Young Australian Adults: Qualitative Study.

Journal of medical Internet research
BACKGROUND: Nutrition misinformation is pervasive on frequently accessed online sources such as social media platforms and websites. Young adults are at a high risk of viewing or engaging with this content due to their high internet and social media ...

Alzheimer's disease classification using a hybrid deep learning approach with multi-layer U-net segmentation and XAI driven analysis.

PloS one
Alzheimer's disease (AD) is a neurodegenerative illness causing a significant decrease in cognitive function, and early, accurate diagnosis is of great therapeutic and diagnostic value. Currently, there is promising potential for applying various typ...

Estimating heterogeneous impacts Of subsidised health insurance: A causal machine learning approach.

PloS one
The evaluation of social and health policies often necessitates understanding the variations in impacts based on recipients' observed characteristics, underscoring the value of estimating treatment effect heterogeneity. In this study, we leverage pre...

The Impact of Comorbidity Patterns on Clinical Outcomes in Heart Failure: A Machine Learning-Based Cluster Analysis.

The American journal of cardiology
Heart failure (HF) is a major global health burden, and complex comorbidity patterns can worsen clinical outcomes and complicate patient care. This study aimed to identify distinct comorbidity-based clusters among HF patients and evaluate their assoc...

In silico purification improves DNA methylation-based classification rates of pediatric low-grade gliomas.

Acta neuropathologica
DNA methylation-based classification using the Heidelberg Classifier is a state-of-the-art data-driven method for molecular diagnosis of central nervous system (CNS) tumors. However, many pediatric low-grade glioma (pLGG) samples fail to yield a conf...