AIMC Topic: Anticoagulants

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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...

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...

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...

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...

Danger, Danger, Gaston Labat! Does zero-shot artificial intelligence correlate with anticoagulation guidelines recommendations for neuraxial anesthesia?

Regional anesthesia and pain medicine
INTRODUCTION: Artificial intelligence and large language models (LLMs) have emerged as potentially disruptive technologies in healthcare. In this study GPT-3.5, an accessible LLM, was assessed for its accuracy and reliability in performing guideline-...

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

Blood purification
INTRODUCTION: Continuous renal replacement therapy (CRRT) is a prolonged continuous extracorporeal blood purification therapy to replace impaired renal function. Typically, CRRT therapy requires routine anticoagulation, but for patients at risk of bl...

Oral anticoagulant treatment in atrial fibrillation: the AFIRMA real-world study using natural language processing and machine learning.

Revista clinica espanola
INTRODUCTION: Oral anticoagulation (OAC) is key in atrial fibrillation (AF) thromboprophylaxis, but Spain lacks substantial real-world evidence. We aimed to analyze the prevalence, clinical characteristics, and treatment patterns among patients with ...

Pleiotropic Effects of Direct Oral Anticoagulants in Chronic Heart Failure and Atrial Fibrillation: Machine Learning Analysis.

Molecules (Basel, Switzerland)
Oral anticoagulant therapy (OAT) for managing atrial fibrillation (AF) encompasses vitamin K antagonists (VKAs, such as warfarin), which was the mainstay of anticoagulation therapy before 2010, and direct-acting oral anticoagulants (DOACs, namely dab...

Machine learning approach for prediction of outcomes in anticoagulated patients with atrial fibrillation.

International journal of cardiology
BACKGROUND: The accuracy of available prediction tools for clinical outcomes in patients with atrial fibrillation (AF) remains modest. Machine Learning (ML) has been used to predict outcomes in the AF population, but not in a population entirely on a...