AIMC Topic: Biomarkers

Clear Filters Showing 31 to 40 of 2219 articles

Machine learning model of clinical laboratory data for 30-day mortality of patients with hodgkin's lymphoma in ICU: a retrospective study based on MIMIC-IV database.

Clinical and experimental medicine
Prognostic stratification of Hodgkin lymphoma (HL) patients in ICU remains challenging, with conventional scoring systems often overlooking pathophysiological biomarkers. This retrospective cohort study analyzed 1,908 HL patients from the MIMIC-IV da...

Longitudinal biomarker progression and validation for predicting operational tolerance in a prospective multicenter liver transplantation immunosuppression withdrawal trial.

PloS one
Liver transplantation (LT) is a life-saving treatment for end-stage liver disease, but long-term immunosuppression (IS) is associated with significant side effects. Achieving operational tolerance (OT), where the graft is accepted without IS, remains...

Association of the endothelial activation and stress index with cognitive function in older adults: a cross-sectional study with machine learning.

European journal of medical research
BACKGROUND: Age-associated memory impairment (AAMI) is a predementia state linked to endothelial dysfunction. The endothelial activation and stress index (EASIX) quantifies endothelial injury, yet its association with cognitive function remains unval...

Rapid Detection and Purification of Extracellular Vesicles for Hepatocellular Carcinoma Screening Using a Plasmonic Metasurface Integrated with the Kolmogorov-Arnold Network.

ACS nano
Hepatocellular carcinoma (HCC) is a leading cause of global cancer-related mortality, with delayed diagnosis adversely affecting patient outcomes. Liquid biopsy techniques using small extracellular vesicles (EVs) offer potential for cancer detection,...

An Improved Deep Semi-supervised JNMF Method for Biomarker Extraction of Alzheimer's Disease.

Journal of molecular neuroscience : MN
Imaging genetics is an approach that explores the underlying mechanisms of brain disorders such as Alzheimer's disease (AD) by analyzing the correlation between neuroimaging and genetic data. Traditional non-negative matrix factorization (NMF) algori...

AI-Assisted Microfluidic Paper-Based Analytical Device with Au-Pt Nanoparticles for Multiplex, Interference-Resistant Quantification of Urinary Biomarkers.

Analytical chemistry
Urinary glucose, creatinine, and uric acid are vital biomarkers for diabetes and kidney disease management. However, multiplex point-of-care detection faces challenges due to insufficient sensitivity in complex urine matrices and signal cross-talk fr...

The Omics Molecule Extractor: A Web Application for the Selection of Potential Biomarker Panels.

Journal of proteome research
Selecting molecular panels that are applicable to classify the health state of patients is a common task in omics data analysis. Existing software for molecule selection lacks features to select molecule panels from large data sets, requires programm...

Accuracy is not enough: explainable boosting machine model and identification of candidate biomarkers for real-time sepsis risk assessment in the emergency department.

BMC emergency medicine
BACKGROUND: Sepsis poses a significant threat in emergency settings, necessitating tools for early and interpretable risk assessment. This study aimed to develop a robust explainable boosting machine (EBM) model, one of the explainable artificial int...

Predictive biomarkers validation of CD3 cell apheresis yield in CAR-T manufacturing for diffuse large B-cell lymphoma: a machine learning approach.

Scientific reports
Chimeric antigen receptor (CAR) T-cell therapy has shown significant success in treating diffuse large B-cell lymphoma (DLBCL). The initial step involves collecting autologous CD3 lymphocytes through apheresis, in which obtaining an adequate CD3 cell...

Context matters in machine learning based disease prediction with insights from diverse clinical and symptom data.

Scientific reports
Machine learning (ML) has the potential to drastically improve clinical decision-making by predicting diseases early, accurately, and based on data. This study evaluated and compared the performance of several machine learning models, including a fee...