Venous Thrombosis

Evaluation of Unfractionated Heparin Dosing by Antifactor Xa During Targeted Temperature Management Post Cardiac Arrest.

PURPOSE: To evaluate unfractionated heparin (UFH) dosing guided by antifactor Xa levels during targeted temperature management (TTM) post-cardiac arrest.

Machine Learning: An Overview and Applications in Pharmacogenetics.

This narrative review aims to provide an overview of the main Machine Learning (ML) techniques and their applications in pharmacogenetics (such as antidepressant, anti-cancer and warfarin drugs) over the past 10 years. ML deals with the study, the design and the development of algorithms that give computers capability to learn without being explicitly programmed. ML is a sub-field of artificial in...

Deep Semantic Segmentation Feature-Based Radiomics for the Classification Tasks in Medical Image Analysis.

Recently, an emerging trend in medical image classification is to combine radiomics framework with deep learning classification network in an integrat...

Development of a system to support warfarin dose decisions using deep neural networks.

The first aim of this study was to develop a prothrombin time international normalized ratio (PT INR) prediction model. The second aim was to develop ...

An artificial neural network approach integrating plasma proteomics and genetic data identifies PLXNA4 as a new susceptibility locus for pulmonary embolism.

Venous thromboembolism is the third common cardiovascular disease and is composed of two entities, deep vein thrombosis (DVT) and its potential fatal ...

Prediction of venous thromboembolism with machine learning techniques in young-middle-aged inpatients.

Accumulating studies appear to suggest that the risk factors for venous thromboembolism (VTE) among young-middle-aged inpatients are different from th...

Deep learning-based automated detection of pulmonary embolism on CT pulmonary angiograms: No significant effects on report communication times and patient turnaround in the emergency department nine months after technical implementation.

OBJECTIVES: Rapid communication of CT exams positive for pulmonary embolism (PE) is crucial for timely initiation of anticoagulation and patient outco...

Systematic review of machine learning models for personalised dosing of heparin.

AIM: To identify and critically appraise studies of prediction models, developed using machine learning (ML) methods, for determining the optimal dosi...

Profiling SARS-CoV-2 Main Protease (M) Binding to Repurposed Drugs Using Molecular Dynamics Simulations in Classical and Neural Network-Trained Force Fields.

The current COVID-19 pandemic caused by a novel coronavirus SARS-CoV-2 urgently calls for a working therapeutic. Here, we report a computation-based w...

Continuous Infusion Low-Dose Unfractionated Heparin for the Management of Hypercoagulability Associated With COVID-19.

INTRODUCTION: The Coronavirus Disease 2019 (COVID-19) is associated with severe hypercoagulability. There is currently limited evidence supporting the...

Risks and complications of robot-assisted radical prostatectomy (RARP) in patients receiving antiplatelet and/or anticoagulant therapy: a retrospective cohort study in a single institute.

The objective of the study was to evaluate the risk of bleeding complications in patients undergoing robot-assisted radical prostatectomy (RARP) while...

Predicting Anticoagulation Need for Otogenic Intracranial Sinus Thrombosis: A Machine Learning Approach.

 The role of anticoagulation (AC) in the management of otogenic cerebral venous sinus thrombosis (OCVST) remains controversial. Our study aims to bet...

Efficacy of enoxaparin in preventing coagulation during high-flux haemodialysis, expanded haemodialysis and haemodiafiltration.

BACKGROUND: Low-molecular-weight heparins (LMWHs) are easily dialysable with high-flow membranes; however, it is not clear whether the LMWH dose shoul...

Mechanical heart valves and pregnancy: Issues surrounding anticoagulation. Experience from two obstetric cardiac centres.

BACKGROUND: Pregnant women with mechanical heart valves are at significant risk of obstetric/cardiac complications. This study compares the anticoagul...

Sulodexide modulates the dialysate effect on the peritoneal mesothelium.

Peritoneal membrane damage during chronic peritoneal dialysis is the main cause of that treatment failure. Preservation of the mesothelial cells (MC) ...

Machine learning detection of Atrial Fibrillation using wearable technology.

BACKGROUND: Atrial Fibrillation is the most common arrhythmia worldwide with a global age adjusted prevalence of 0.5% in 2010. Anticoagulation treatme...

Investigating the use of data-driven artificial intelligence in computerised decision support systems for health and social care: A systematic review.

There is growing interest in the potential of artificial intelligence to support decision-making in health and social care settings. There is, however...

Machine Learning Algorithm for Predicting Warfarin Dose in Caribbean Hispanics Using Pharmacogenetic Data.

Despite some previous examples of successful application to the field of pharmacogenomics, the utility of machine learning (ML) techniques for warfari...

Predicting Chronic Subdural Hematoma Recurrence and Stroke Outcomes While Withholding Antiplatelet and Anticoagulant Agents.

The aging of the western population and the increased use of oral anticoagulation (OAC) and antiplatelet drugs (APD) will result in a clinical dilemm...

Assessment of a Machine Learning Model Applied to Harmonized Electronic Health Record Data for the Prediction of Incident Atrial Fibrillation.

IMPORTANCE: Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia, and its early detection could lead to significant improvements i...