Cardiovascular

Venous Thrombosis

Latest AI and machine learning research in venous thrombosis for healthcare professionals.

1,841 articles
Stay Ahead - Weekly Venous Thrombosis research updates
Subscribe
Browse Specialties
Showing 1-21 of 1,841 articles
Analysis of aPTT predictors after unfractionated heparin administration in intensive care units using machine learning models.

OBJECTIVES: Predicting optimal coagulation control using heparin in intensive care units (ICUs) rema...

Deep learning reconstruction enhances image quality in contrast-enhanced CT venography for deep vein thrombosis.

PURPOSE: This study aimed to evaluate and compare the diagnostic performance and image quality of de...

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 intracereb...

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 as...

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...

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...

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 s...

Invited Article: Al guided Dual Antiplatelet Therapy and Anticoagulation.

Artificial intelligence (AI) has emerged as a transformative tool in healthcare through data analysi...

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 ult...

Cangrelor and AVN-944 as repurposable candidate drugs for hMPV: analysis entailed by AI-driven in silico approach.

Human metapneumovirus (hMPV) primarily causes respiratory tract infections in young children and old...

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...

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 prescriptio...

Predicting a failure of postoperative thromboprophylaxis in non-small cell lung cancer: A stacking machine learning approach.

BACKGROUND: Non-small-cell lung cancer (NSCLC) and its surgery significantly increase the venous thr...

An Early Thyroid Screening Model Based on Transformer and Secondary Transfer Learning for Chest and Thyroid CT Images.

IntroductionThyroid cancer is a common malignant tumor, and early diagnosis and timely treatment are...

Development of an artificial intelligence-enhanced warfarin interaction checker platform.

Warfarin is a common anticoagulant drug for thrombo-prophylaxis in stroke and venous thromboembolism...

RSM and AI based machine learning for quality by design development of rivaroxaban push-pull osmotic tablets and its PBPK modeling.

The study is based on applying Artificial Neural Network (ANN) based machine learning and Response S...

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

Predicting major bleeding in nonvalvular atrial fibrillation (AF) patients on direct oral anticoagul...

Utilizing 12-lead electrocardiogram and machine learning to retrospectively estimate and prospectively predict atrial fibrillation and stroke risk.

BACKGROUND: The stroke risk in patients with subclinical atrial fibrillation (AF) is underestimated....

Browse Specialties