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Anticoagulants

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Heparin in sepsis: current clinical findings and possible mechanisms.

Frontiers in immunology
Sepsis is a clinical syndrome resulting from the interaction between coagulation, inflammation, immunity and other systems. Coagulation activation is an initial factor for sepsis to develop into multiple organ dysfunction. Therefore, anticoagulant th...

Prediction model for major bleeding in anticoagulated patients with cancer-associated venous thromboembolism using machine learning and natural language processing.

Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico
PURPOSE: We developed a predictive model to assess the risk of major bleeding (MB) within 6 months of primary venous thromboembolism (VTE) in cancer patients receiving anticoagulant treatment. We also sought to describe the prevalence and incidence o...

A Machine Learning-Based Approach for the Prediction of Anticoagulant Activity of Hypericum perforatum L. and Evaluation of Compound Activity.

Phytochemical analysis : PCA
INTRODUCTION: Hypericum perforatum L. (HPL) is extensively researched domestically and internationally as a medicinal plant. However, no reports of studies related to the anticoagulant activity of HPL have been retrieved. The specific bioactive compo...

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

Neurologia medico-chirurgica
This study aimed to investigate the patterns of anticoagulation therapy and coagulation parameters and to develop a prediction model to predict the type of anticoagulation therapy in geriatric patients with traumatic brain injury. A retrospective ana...

Residual risk prediction in anticoagulated patients with atrial fibrillation using machine learning: A report from the GLORIA-AF registry phase II/III.

European journal of clinical investigation
BACKGROUND: Although oral anticoagulation decreases the risk of thromboembolism in patients with atrial fibrillation (AF), a residual risk of thrombotic events still exists. This study aimed to construct machine learning (ML) models to predict the re...

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

Hamostaseologie
The high incidence of venous thromboembolism (VTE) globally and the morbidity and mortality burden associated with the disease make it a pressing issue. Machine learning (ML) can improve VTE prevention, detection, and treatment. The ability of this n...

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

Computers in biology and medicine
This study highlights the importance of evaluating warfarin dosing in diabetic patients, who require careful anticoagulation management. With rising rates of diabetes and cardiovascular diseases, understanding the factors influencing warfarin therapy...

Machine Learning Predicts Bleeding Risk in Atrial Fibrillation Patients on Direct Oral Anticoagulant.

The American journal of cardiology
Predicting major bleeding in nonvalvular atrial fibrillation (AF) patients on direct oral anticoagulants (DOACs) is crucial for personalized care. Alternatives like left atrial appendage closure devices lower stroke risk with fewer nonprocedural blee...

Anticoagulation colloidal microrobots based on heparin-mimicking polymers.

Journal of colloid and interface science
Coagulation within blood vessels is a major cause of cardiovascular disease and global mortality, highlighting the urgent need for effective anticoagulant strategies. In this study, we introduce a dynamic and highly efficient anticoagulant platform, ...