AIMC Topic: Venous Thromboembolism

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Non-weight-based enoxaparin dosing subtherapeutic in trauma patients.

The Journal of surgical research
BACKGROUND: We report our experience dosing and monitoring enoxaparin with anti-factor Xa activity (anti-FXaA) levels for venous thromboembolism prophylaxis in trauma patients (TP).

A novel method of adverse event detection can accurately identify venous thromboembolisms (VTEs) from narrative electronic health record data.

Journal of the American Medical Informatics Association : JAMIA
BACKGROUND: Venous thromboembolisms (VTEs), which include deep vein thrombosis (DVT) and pulmonary embolism (PE), are associated with significant mortality, morbidity, and cost in hospitalized patients. To evaluate the success of preventive measures,...

SSA-sMLP: A venous thromboembolism risk prediction model using separable self-attention and spatial-shift multilayer perceptrons.

Thrombosis research
Accurate risk assessment of Venous Thromboembolism (VTE) holds significant value for clinical decision-making. However, traditional scoring systems relying on linear assumptions and expert experience, along with machine learning models constrained by...

Evaluation of risk factors for thromboembolic events in multiple myeloma patients using multiple machine learning models.

Medicine
Venous thromboembolic events (VTE) is a frequent complication in multiple myeloma (MM) patients, raising mortality. This study aims to use machine learning to identify VTE risk factors in MM, helping to pinpoint high-risk individuals for better clini...

Deep Learning-Based Dynamic Risk Prediction of Venous Thromboembolism for Patients With Ovarian Cancer in Real-World Settings From Electronic Health Records.

JCO clinical cancer informatics
PURPOSE: Patients with epithelial ovarian cancer (EOC) have an elevated risk for venous thromboembolism (VTE). To assess the risk of VTE, models were developed by statistical or machine learning algorithms. However, few models have accommodated deep ...

Machine learning natural language processing for identifying venous thromboembolism: systematic review and meta-analysis.

Blood advances
Venous thromboembolism (VTE) is a leading cause of preventable in-hospital mortality. Monitoring VTE cases is limited by the challenges of manual medical record review and diagnosis code interpretation. Natural language processing (NLP) can automate ...

Extended Venous Thromboembolism Prophylaxis after Robotic Staging for Endometrial Cancer.

Southern medical journal
OBJECTIVES: Our objectives were to estimate the incidence of venous thromboembolism (VTE) after robotic staging for endometrial cancer and to compare the incidence of VTE in patients who received a single dose of preoperative prophylaxis of enoxapari...

Low Risk of Venous Thromboembolism After Robot-assisted Radical Prostatectomy Through Systemic Image Assessment: A Prospective Study.

In vivo (Athens, Greece)
BACKGROUND/AIM: The aim of the study was to evaluate the risk of venous thromboembolism (VTE) after robot-assisted radical prostatectomy (RARP) and discuss whether a uniform prophylaxis for VTE after radical prostatectomy is also suitable for robotic...

Prediction and Diagnosis of Venous Thromboembolism Using Artificial Intelligence Approaches: A Systematic Review and Meta-Analysis.

Clinical and applied thrombosis/hemostasis : official journal of the International Academy of Clinical and Applied Thrombosis/Hemostasis
Venous thromboembolism (VTE) is a fatal disease and has become a burden on the global health system. Recent studies have suggested that artificial intelligence (AI) could be used to make a diagnosis and predict venous thrombosis more accurately. Thus...