AIMC Topic: Thrombosis
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Programmable ultrasound-mediated swarms manipulation of bacteria-red blood cell microrobots for tumor-specific thrombosis and robust photothermal therapy.
Trends in biotechnology
Dec 20, 2024
Despite the excellent advantages of biomicrorobots, such as autonomous navigation and targeting actuation, effective penetration and retention to deep lesion sites for effective therapy remains a longstanding challenge. Here, we present dual-engine c...
Mapping Thrombosis Serum Markers by H-NMR Allied with Machine Learning Tools.
Molecules (Basel, Switzerland)
Dec 13, 2024
Machine learning and artificial intelligence tools were used to investigate the discriminatory potential of blood serum metabolites for thromboembolism and antiphospholipid syndrome (APS). H-NMR-based metabonomics data of the serum samples of patient...
Residual risk prediction in anticoagulated patients with atrial fibrillation using machine learning: A report from the GLORIA-AF registry phase II/III.
European journal of clinical investigation
Dec 11, 2024
BACKGROUND: Although oral anticoagulation decreases the risk of thromboembolism in patients with atrial fibrillation (AF), a residual risk of thrombotic events still exists. This study aimed to construct machine learning (ML) models to predict the re...
Risk Factors
Aged
Aged, 80 and over
Male
Humans
Machine Learning
Algorithms
Female
Logistic Models
Administration, Oral
Registries
Ischemic Stroke
Atrial Fibrillation
Stroke
Area Under Curve
Ischemic Attack, Transient
Myocardial Infarction
Thromboembolism
Anticoagulants
Thrombosis
Middle Aged
Risk Assessment
ROC Curve
Machine learning models for risk prediction of cancer-associated thrombosis: a systematic review and meta-analysis.
Journal of thrombosis and haemostasis : JTH
Nov 15, 2024
BACKGROUND: Although the number of models for predicting the risk of cancer-associated thrombosis has been rising, there is still a lack of comprehensive assessment for machine learning prediction models.
Machine-Learning Applications in Thrombosis and Hemostasis.
Hamostaseologie
Nov 5, 2024
The use of machine-learning (ML) algorithms in medicine has sparked a heated discussion. It is considered one of the most disruptive general-purpose technologies in decades. It has already permeated many areas of our daily lives and produced applicat...
Construction and validation of a nomogram prediction model for the catheter-related thrombosis risk of central venous access devices in patients with cancer: a prospective machine learning study.
Journal of thrombosis and thrombolysis
Oct 3, 2024
Central venous access devices (CVADs) are integral to cancer treatment. However, catheter-related thrombosis (CRT) poses a considerable risk to patient safety. It interrupts treatment; delays therapy; prolongs hospitalisation; and increases the physi...
Untethered & Stiffness-Tunable Ferromagnetic Liquid Robots for Cleaning Thrombus in Complex Blood Vessels.
Advanced materials (Deerfield Beach, Fla.)
Sep 23, 2024
Thrombosis is a significant threat to human health. However, the existing clinical treatment methods have limitations. Magnetic soft matter is used in the biomedical field for years, and ferromagnetic liquids exhibit tunable stiffness and on-demand m...
Construction of Risk-Prediction Models for Autogenous Arteriovenous Fistula Thrombosis in Patients on Maintenance Hemodialysis.
Blood purification
Aug 1, 2024
INTRODUCTION: Autogenous arteriovenous fistula (AVF) is the preferred vascular access in patients undergoing maintenance hemodialysis (MHD). However, complications such as thrombosis may occur. This study aimed to construct and validate a machine lea...
Predictors of left atrial appendage thrombus in atrial fibrillation patients undergoing cardioversion.
Journal of interventional cardiac electrophysiology : an international journal of arrhythmias and pacing
Jul 12, 2024
BACKGROUND: Atrial fibrillation and atrial flutter represent the most prevalent clinically significant cardiac arrhythmias. While the CHA2DS2-VASc score is commonly used to inform anticoagulation therapy decisions for patients with these conditions, ...
Machine Learning-based Framework Develops a Tumor Thrombus Coagulation Signature in Multicenter Cohorts for Renal Cancer.
International journal of biological sciences
Jul 1, 2024
Renal cell carcinoma (RCC) is frequently accompanied by tumor thrombus in the venous system with an extremely dismal prognosis. The current Tumor Node Metastasis (TNM) stage and Mayo clinical classification do not appropriately identify preference-s...
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