Cardiovascular

Venous Thrombosis

Latest AI and machine learning research in venous thrombosis for healthcare professionals.

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Showing 22-42 of 1,841 articles
Causal Machine Learning for Left Atrial Appendage Occlusion in Patients With Atrial Fibrillation.

BACKGROUND: Transcatheter left atrial appendage occlusion (LAAO) is an alternative to lifelong antic...

Correlation of rivaroxaban solubility in mixed solvents for optimization of solubility using machine learning analysis and validation.

In this study, the solubility of rivaroxaban, a poorly water-soluble drug, was investigated in mixed...

Machine learning assisted radiomics in predicting postoperative occurrence of deep venous thrombosis in patients with gastric cancer.

BACKGROUND: Gastric cancer patients are prone to lower extremity deep vein thrombosis (DVT) after su...

Optimizing warfarin dosing in diabetic patients through BERT model and machine learning techniques.

This study highlights the importance of evaluating warfarin dosing in diabetic patients, who require...

Machine learning analysis of rivaroxaban solubility in mixed solvents for application in pharmaceutical crystallization.

This study investigates the use of machine learning models to predict solubility of rivaroxaban in b...

Machine Learning-Assisted Biomass-Derived Carbon Dots as Fluorescent Sensor Array for Discrimination of Warfarin and Its Metabolites.

Warfarin (WAR), an effective oral anticoagulant, is of utmost importance in treating many diseases. ...

Preparation and optimisation of solid lipid nanoparticles of rivaroxaban using artificial neural networks and response surface method.

AIMS: This study aimed to improve rivaroxaban delivery by optimising solid lipid nanoparticles (SLN)...

Innovative modified-net architecture: enhanced segmentation of deep vein thrombosis.

A new era for diagnosing and treating Deep Vein Thrombosis (DVT) relies on precise segmentation from...

The predictive value of heparin-binding protein for bacterial infections in patients with severe polytrauma.

INTRODUCTION: Heparin-binding protein is an inflammatory factor with predictive value for sepsis and...

Development of Machine-learning Model to Predict Anticoagulant Use and Type in Geriatric Traumatic Brain Injury Using Coagulation Parameters.

This study aimed to investigate the patterns of anticoagulation therapy and coagulation parameters a...

From Code to Clots: Applying Machine Learning to Clinical Aspects of Venous Thromboembolism Prevention, Diagnosis, and Management.

The high incidence of venous thromboembolism (VTE) globally and the morbidity and mortality burden a...

Heparin in sepsis: current clinical findings and possible mechanisms.

Sepsis is a clinical syndrome resulting from the interaction between coagulation, inflammation, immu...

Construction and verification of a machine learning-based prediction model of deep vein thrombosis formation after spinal surgery.

BACKGROUND: Deep vein thromboembolism (DVT) is a common postoperative complication with high morbidi...

Novel Strategy for Human Deep Vein Thrombosis Diagnosis Based on Metabolomics and Stacking Machine Learning.

Deep vein thrombosis (DVT) is a serious health issue that often leads to considerable morbidity and ...

Coagulation Risk Predicting in Anticoagulant-Free Continuous Renal Replacement Therapy.

INTRODUCTION: Continuous renal replacement therapy (CRRT) is a prolonged continuous extracorporeal b...

Single-center outcomes of artificial intelligence in management of pulmonary embolism and pulmonary embolism response team activation.

Multidisciplinary pulmonary embolism response teams (PERTs) have shown that timely triage expedites ...

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