Peptic Ulcer Disease

Integrative machine learning reveals hidden and emerging co-regulatory gene networks for multi-phase glioblastoma outcome prediction.

BACKGROUND: Glioblastoma (GBM) is a highly prevalent and aggressive type of brain tumor characterized by profound molecular complexity and poor prognosis. While conventional biomarker studies focus on highly significant genes or proteins associated with cancer outcomes, the contribution of gene-gene co-regulation to GBM progression remains unclear. METHODS: This study employs an application of int...

Risk Prediction for Delayed Bleeding After Endoscopic Submucosal Dissection in Colorectal Precancerous Lesions Using Artificial Intelligence: Retrospective Clinical Trial.

BACKGROUND: Delayed bleeding is a common complication after endoscopic submucosal dissection. OBJECTIVE: Our study aimed to assess risk factors for delayed bleeding after endoscopic submucosal dissection in patients with colorectal precancerous lesions and to construct a risk prediction model. DESIGN: Retrospective case-control study. SETTINGS: Multicenter study. PATIENTS: A total of 956 patients ...

Label Noise in Pathological Segmentation Is Overlooked, Leading to Potential Overestimation of Artificial Intelligence.

Artificial intelligence (AI) has transformed medical imaging, notably in radiology and endoscopy. Semantic segmentation, a pixel-level technique cruci...

DSSA-PPI: enhancing binding affinity change prediction upon protein mutations using disentangled structure-sequence aware attention.

Accurate assessment of the effects of mutations on protein-protein interactions (PPIs) is crucial for understanding disease pathogenesis and the devel...

Predicting postoperative length of stay: a feature selection approach to predictive modeling in lumbar fusion surgery.

BACKGROUND: Predictive modeling has the potential to improve preoperative planning and resource allocation in lumbar fusion surgery. This study aimed ...

Exploring shared diagnostic genes and molecular mechanisms in multiple sclerosis and psoriasis through bioinformatics analysis and machine learning.

BACKGROUND: Multiple sclerosis (MS) and psoriasis are both immune-mediated inflammatory diseases. Previous studies have reported comorbidity between M...

Artificial intelligence in endoscopy: a yet underused but promising tool in underserved settings.

Sociodemographic Predictors of Child-Parent Agreement on Oral Health.

BACKGROUND: Oral health is vital for children's overall well-being. Parents play a critical role in shaping children's oral health through preventive ...

Literature-informed ensemble machine learning for three-year diabetic kidney disease risk prediction in type 2 diabetes: Development, validation, and deployment of the PSMMC NephraRisk model.

INTRODUCTION: Diabetic kidney disease (DKD) and diabetic nephropathy (DN) affect around 40% of diabetic patients but lack accurate risk prediction too...

Comparative Performance of Machine Learning and Traditional Risk Scores in Predicting Adverse Events After Transcatheter Aortic Valve Replacement in Patients With Atrial Fibrillation.

Patients with atrial fibrillation (AF) following transcatheter aortic valve replacement (TAVR) remain at risk of ischemic stroke (IS) and bleeding. Ho...

Creating Solutions to the Challenges in Managing Heparin Infusions: Exploring Use of Technology Including Artificial Intelligence and the Role of Anticoagulation Stewardship.

Adverse events and medication-related errors are common with anticoagulants. Heparin infusions are frequently associated with administration errors or...

AI-powered intestinal ultrasound in inflammatory bowel disease: Advancing toward automated disease assessment.

The management of inflammatory bowel disease (IBD) relies on accurate disease assessment and close monitoring to guide therapy and evaluate treatment ...

Correction: Curriculum for safe and effective use of artificial intelligence in endoscopy: European Society of Gastrointestinal Endoscopy (ESGE) Position Statement.

Curriculum for safe and effective use of artificial intelligence in endoscopy: European Society of Gastrointestinal Endoscopy (ESGE) Position Statement.

The European Society of Gastrointestinal Endoscopy (ESGE) has identified a critical need to establish structured training for safe and effective use o...

Discovery of hemostatic component combination from Nelumbinis Receptaculum using dual machine learning spectrum-effect analysis.

ETHNOPHARMACOLOGICAL RELEVANCE: Nelumbinis Receptaculum (NR) were hemostatic herbal medicines for treating metrorrhagia, hematuria or hemorrhoids in e...

Developing a Statistical Modeling-Based Machine Learning Approach for Capturing Drug Dosing Using a Proton Pump Inhibitor Case.

OBJECTIVE: To develop a statistical model to capture medication dosing for proton pump inhibitors (PPIs) using structured data from electronic health ...

Emerging risk factors for stroke and bleeding in patients with atrial fibrillation and heart failure-a narrative review.

Atrial fibrillation (AF) and heart failure (HF) frequently coexist, which leads to adverse clinical outcomes and a significant increase in the risk of...

Functional profiling of the chaperone systems interactome in breast cancer using experimental and machine-learning data.

Breast cancer heterogeneity stems from diverse molecular alterations, including proteostasis loss due to chaperone system dysfunction. However, the im...

Identification of common genetic features and pathways for osteoporosis with frozen shoulder by integrated bioinformatics analysis and machine learning.

Frozen shoulder (FS) and osteoporosis (OP) are common age-related degenerative diseases, occurring more frequently in females, which suggests potentia...

An Interpretable Machine Learning Model Based on MRI Features for Predicting Pain Severity in Temporomandibular Disorders.

BACKGROUND: Chronic pain around the temporomandibular joint (TMJ) and masticatory muscles is a primary symptom of temporomandibular disorders (TMD). H...