AIMC Topic: Humans

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Identifying disease progression biomarkers in metabolic associated steatotic liver disease (MASLD) through weighted gene co-expression network analysis and machine learning.

Journal of translational medicine
BACKGROUND: Metabolic Associated Steatotic Liver Disease (MASLD), encompassing conditions simple liver steatosis (MAFL) and metabolic associated steatohepatitis (MASH), is the most prevalent chronic liver disease. Currently, the management of MASLD i...

Generative artificial intelligence in physiotherapy education: great potential amidst challenges- a qualitative interview study.

BMC medical education
BACKGROUND: Generative Artificial Intelligence (GAI) has significantly impacted education at all levels, including health professional education. Understanding students' experiences is essential to enhancing AI literacy, adapting education to GAI, an...

A cross-sectional study on ChatGPT's alignment with clinical practice guidelines in musculoskeletal rehabilitation.

BMC musculoskeletal disorders
BACKGROUND: AI models like ChatGPT have the potential to support musculoskeletal rehabilitation by providing clinical insights. However, their alignment with evidence-based guidelines needs evaluation before integration into physiotherapy practice.

Exploring the potential and limitations of deep learning and explainable AI for longitudinal life course analysis.

BMC public health
BACKGROUND: Understanding the complex interplay between life course exposures, such as adverse childhood experiences and environmental factors, and disease risk is essential for developing effective public health interventions. Traditional epidemiolo...

An investigation into the impact of temporality on COVID-19 infection and mortality predictions: new perspective based on Shapley Values.

BMC medical research methodology
INTRODUCTION: Machine learning models have been employed to predict COVID-19 infections and mortality, but many models were built on training and testing sets from different periods. The purpose of this study is to investigate the impact of temporali...

Prediction of significant congenital heart disease in infants and children using continuous wavelet transform and deep convolutional neural network with 12-lead electrocardiogram.

BMC pediatrics
BACKGROUND: Congenital heart disease (CHD) affects approximately 1% of newborns and is a leading cause of mortality in early childhood. Despite the importance of early detection, current screening methods, such as pulse oximetry and auscultation, hav...

Constructing machine learning-based risk prediction model for osteoarthritis in population aged 45 and above: NHANES 2011-2018.

Scientific reports
Osteoarthritis is a widespread chronic joint disease, becoming increasingly prevalent, particularly among individuals over the age of 45. This condition causes joint pain and dysfunction, significantly disrupting daily life. The objective of this stu...

Exploring hypoxia driven subtypes of pulmonary arterial hypertension through transcriptomics single cell sequencing and machine learning.

Scientific reports
Pulmonary arterial hypertension (PAH) is a progressive cardiovascular disease characterized by elevated pulmonary arterial pressure, leading to right heart failure and death. Despite advancements in diagnosis and treatment, it remains incurable, and ...

Leveraging TME features and multi-omics data with an advanced deep learning framework for improved Cancer survival prediction.

Scientific reports
Glioma, a malignant intracranial tumor with high invasiveness and heterogeneity, significantly impacts patient survival. This study integrates multi-omics data to improve prognostic prediction and identify therapeutic targets. Using single-cell data ...

Predicting outcomes following open abdominal aortic aneurysm repair using machine learning.

Scientific reports
Patients undergoing open surgical repair of abdominal aortic aneurysm (AAA) have a high risk of post-operative complications. However, there are no widely used tools to predict surgical risk in this population. We used machine learning (ML) technique...