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Hemorrhage

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Research on workflow recognition for liver rupture repair surgery.

Mathematical biosciences and engineering : MBE
Liver rupture repair surgery serves as one tool to treat liver rupture, especially beneficial for cases of mild liver rupture hemorrhage. Liver rupture can catalyze critical conditions such as hemorrhage and shock. Surgical workflow recognition in li...

Machine learning in the prediction of massive transfusion in trauma: a retrospective analysis as a proof-of-concept.

European journal of trauma and emergency surgery : official publication of the European Trauma Society
PURPOSE: Early administration and protocolization of massive hemorrhage protocols (MHP) has been associated with decreases in mortality, multiorgan system failure, and number of blood products used. Various prediction tools have been developed for th...

Effects of thienopyridine class antiplatelets on bleeding outcomes following robot-assisted radical prostatectomy.

Scientific reports
This study aimed to assess the effects of thienopyridine-class antiplatelet agents (including ticlopidine, clopidogrel, and prasugrel) on bleeding complications in patients who underwent robot-assisted radical prostatectomy. This cohort study used a ...

Machine learning approach for prediction of outcomes in anticoagulated patients with atrial fibrillation.

International journal of cardiology
BACKGROUND: The accuracy of available prediction tools for clinical outcomes in patients with atrial fibrillation (AF) remains modest. Machine Learning (ML) has been used to predict outcomes in the AF population, but not in a population entirely on a...

A deep-learning approach to predict bleeding risk over time in patients on extended anticoagulation therapy.

Journal of thrombosis and haemostasis : JTH
BACKGROUND: Thus far, all the clinical models developed to predict major bleeding in patients on extended anticoagulation therapy use the baseline predictors to stratify patients into different risk groups. Therefore, these models do not account for ...

Predicting blood transfusion following traumatic injury using machine learning models: A systematic review and narrative synthesis.

The journal of trauma and acute care surgery
BACKGROUND: Hemorrhage is a leading cause of preventable death in trauma. Accurately predicting a patient's blood transfusion requirement is essential but can be difficult. Machine learning (ML) is a field of artificial intelligence that is emerging ...

Prediction of Occult Hemorrhage in the Lower Body Negative Pressure Model: Initial Validation of Machine Learning Approaches.

Military medicine
INTRODUCTION: Detection of occult hemorrhage (OH) before progression to clinically apparent changes in vital signs remains an important clinical problem in managing trauma patients. The resource-intensiveness associated with continuous clinical patie...

Prediction model for major bleeding in anticoagulated patients with cancer-associated venous thromboembolism using machine learning and natural language processing.

Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico
PURPOSE: We developed a predictive model to assess the risk of major bleeding (MB) within 6 months of primary venous thromboembolism (VTE) in cancer patients receiving anticoagulant treatment. We also sought to describe the prevalence and incidence o...

Integrating chemokines and machine learning algorithms for diagnosis and bleeding assessment in primary immune thrombocytopenia: A prospective cohort study.

British journal of haematology
Primary immune thrombocytopenia (ITP) is an autoimmune bleeding disorder, and chemokines have been shown to be dysregulated in autoimmune disorders. We conducted a prospective analysis to identify potential chemokines that could enhance the diagnosti...

Danger, Danger, Gaston Labat! Does zero-shot artificial intelligence correlate with anticoagulation guidelines recommendations for neuraxial anesthesia?

Regional anesthesia and pain medicine
INTRODUCTION: Artificial intelligence and large language models (LLMs) have emerged as potentially disruptive technologies in healthcare. In this study GPT-3.5, an accessible LLM, was assessed for its accuracy and reliability in performing guideline-...