Venous Thrombosis

Systematic review and meta-analysis of AI accuracy in warfarin dose prediction across ethnic groups.

PURPOSE: The primary purpose of this study is to systematically evaluate how accurately artificial intelligence (AI) models can predict optimal warfarin dosing by incorporating both genetic variations-particularly in VKORC1 and CYP2C9-and clinical parameters. METHODS: We searched PubMed, Scopus, and the Cochrane Library from inception until November 2024 for studies using machine learning to estim...

Dynamic Ensemble Selection for Early Detection of Deep Vein Thrombosis in Fracture Patients.

Deep vein thrombosis (DVT) in fracture patients is often clinically silent, with a high incidence of thrombosis and associated mortality. Static machine learning methods struggle to address the challenge of early DVT diagnosis due to their inability to adapt to heterogeneous data across patients. In contrast, Dynamic Ensemble Selection (DES) improves clinical decision-making and therapeutic interv...

Clinical decision support system for detecting right ventricular dysfunction in acute pulmonary embolism: Explainable a new Binary Rule Search (BRS) ensembles and robustness evaluation.

BACKGROUND: Right-ventricular dysfunction (RVD) in acute pulmonary embolism (PE) carries excess short-term mortality; fast, transparent risk stratific...

Improving Genotype Imputation in High-Dimensional Pharmacogenomics Using Multiple Imputation: Evaluation with Machine Learning Approaches.

Multiple imputation is well-established for handling missing data, yet its use in high-dimensional genetic datasets remains limited. Using pharmacokin...

Artificial intelligence and machine learning in thrombosis and hemostasis: a scoping review of clinical and laboratory applications, challenges, and future directions.

This scoping review followed the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guideli...

Creating Solutions to the Challenges in Managing Heparin Infusions: Exploring Use of Technology Including Artificial Intelligence and the Role of Anticoagulation Stewardship.

Adverse events and medication-related errors are common with anticoagulants. Heparin infusions are frequently associated with administration errors or...

Impact of an autonomous delivery robot on sample turnaround time in a clinical laboratory: an early evaluation of first implementation.

OBJECTIVES: The aim of this study was to evaluate the impact of introducing an autonomous courier in a clinical laboratory, focusing on specimen turna...

Optimal adherence thresholds for oral anticoagulants in patients with atrial fibrillation using machine learning and population administrative data.

BACKGROUND: Adherence to oral anticoagulants (OACs) for atrial fibrillation (AF) stroke prevention is traditionally defined as taking 80% of doses as ...

Development of an interpretable machine learning model to predict short-term bleeding risk in patients receiving dual antithrombotic therapy following cardiac surgery.

INTRODUCTION: Bleeding is a serious complication in cardiac surgery, especially among patients receiving combined anticoagulant and antiplatelet thera...

Machine Learning in Venous Thromboembolism-Why and What Next?

Venous thromboembolism (VTE) remains a leading cause of cardiovascular morbidity and mortality, despite advances in imaging and anticoagulation. VTE a...

Machine learning models predict risk of lower extremity deep vein thrombosis in hospitalized patients with spontaneous intracerebral hemorrhage.

Lower extremity deep vein thrombosis is one of the important complications of spontaneous intracerebral hemorrhage. We aimed to develop a risk assessm...

A modified generative adversarial networks method for assisting the diagnosis of deep venous thrombosis complications in stroke patients.

Discriminate deep vein thrombosis, one of the complications in early stroke patients, in order to assist in diagnosis. We have constructed a new metho...

Machine learning application for bleeding risk prediction in patients with atrial fibrillation treated with oral anticoagulation.

Atrial fibrillation (AF) is a prevalent cardiac arrhythmia associated with a significantly increased risk of systemic thromboembolism and stroke. Anti...

The Application of Machine Learning in Warfarin Dose Precision for Diabetic Patients Treated with Statins: A Comparative Study.

PURPOSE: To evaluate the impact of statin therapy on warfarin dose requirements in diabetic patients and to assess the performance of various machine ...

Impact of body mass index on D-dimer diagnostic utility for deep vein thrombosis in patients with cancer: a single-center retrospective analysis.

BACKGROUND: Deep vein thrombosis (DVT) is a common complication in cancer patients associated with significant morbidity and mortality. D-dimer is a w...

Invited Article: Al guided Dual Antiplatelet Therapy and Anticoagulation.

Artificial intelligence (AI) has emerged as a transformative tool in healthcare through data analysis, pattern recognition and predictive modeling cap...

Artificial intelligence guided imaging as a tool to fill gaps in health care delivery.

Deep vein thrombosis (DVT) causes significant morbidity/mortality and timely diagnosis often via ultrasound is critical. However, the shortage of trai...

Comparing machine learning models for predicting preoperative DVT incidence in elderly hypertensive patients with hip fractures: a retrospective analysis.

Hip fractures in the elderly present a significant public health challenge globally, especially among patients with hypertension, who are at an increa...

Deep learning-based classification of lymphedema and other lower limb edema diseases using clinical images.

Lymphedema is a chronic condition characterized by lymphatic fluid accumulation, primarily affecting the limbs. Its diagnosis is challenging due to sy...

Establishing a Validation Framework of Treatment Discontinuation in Claims Data Using Natural Language Processing and Electronic Health Records.

Measuring medication discontinuation in claims data primarily relies on the gaps between prescription fills, but such definitions are rarely validated...