AIMC Topic: Blood Coagulation

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Applying artificial intelligence to uncover the genetic landscape of coagulation factors.

Journal of thrombosis and haemostasis : JTH
Artificial intelligence (AI) is rapidly advancing our ability to identify and interpret genetic variants associated with coagulation factor deficiencies. This review introduces AI, with a specific focus on machine learning (ML) methods, and examines ...

Heparin in sepsis: current clinical findings and possible mechanisms.

Frontiers in immunology
Sepsis is a clinical syndrome resulting from the interaction between coagulation, inflammation, immunity and other systems. Coagulation activation is an initial factor for sepsis to develop into multiple organ dysfunction. Therefore, anticoagulant th...

Coagulo-Net: Enhancing the mathematical modeling of blood coagulation using physics-informed neural networks.

Neural networks : the official journal of the International Neural Network Society
Blood coagulation, which involves a group of complex biochemical reactions, is a crucial step in hemostasis to stop bleeding at the injury site of a blood vessel. Coagulation abnormalities, such as hypercoagulation and hypocoagulation, could either c...

Coagulation Risk Predicting in Anticoagulant-Free Continuous Renal Replacement Therapy.

Blood purification
INTRODUCTION: Continuous renal replacement therapy (CRRT) is a prolonged continuous extracorporeal blood purification therapy to replace impaired renal function. Typically, CRRT therapy requires routine anticoagulation, but for patients at risk of bl...

Machine learning identifies novel coagulation genes as diagnostic and immunological biomarkers in ischemic stroke.

Aging
BACKGROUND: Coagulation system is currently known associated with the development of ischemic stroke (IS). Thus, the current study is designed to identify diagnostic value of coagulation genes (CGs) in IS and to explore their role in the immune micro...

Effect of Retrograde Autologous Priming on Coagulation Assessed by Rotation Thromboelastometry in Patients Undergoing Valvular Cardiac Surgery.

Journal of cardiothoracic and vascular anesthesia
OBJECTIVES: To investigate the effect of retrograde autologous priming (RAP) on coagulation function using rotation thromboelastometry (ROTEM) in patients undergoing valvular cardiac surgery.

Machine learning in the coagulation and hemostasis arena: an overview and evaluation of methods, review of literature, and future directions.

Journal of thrombosis and haemostasis : JTH
Artificial Intelligence and machine-learning (ML) studies are increasingly populating the life science space and some have also started to integrate certain clinical decision support tasks. However, most of the activities within this space understand...

Space-time-regulated imaging analyzer for smart coagulation diagnosis.

Cell reports. Medicine
The development of intelligent blood coagulation diagnoses is awaited to meet the current need for large clinical time-sensitive caseloads due to its efficient and automated diagnoses. Herein, a method is reported and validated to realize it through ...

Combining mathematical modeling and deep learning to make rapid and explainable predictions of the patient-specific response to anticoagulant therapy under venous flow.

Mathematical biosciences
Anticoagulant drugs are commonly prescribed to prevent hypercoagulable states in patients with venous thromboembolism. The choice of the most efficient anticoagulant and the appropriate dosage regimen remain a complex problem because of the intersubj...