AIMC Topic: Hemorrhage
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Modeling the prediction of spontaneous rupture and bleeding in hepatocellular carcinoma via machine learning algorithms.
Scientific reports
Jul 1, 2025
This study aimed to identify the risk factors associated with spontaneous rupture and bleeding in hepatocellular carcinoma, establish a prediction model for spontaneous rupture bleeding via a machine learning algorithm, and validate and evaluate the ...
Gasdermin D drives the systemic storm and mortality after trauma with hemorrhage to a greater degree in biological females than males.
Science translational medicine
May 28, 2025
Severe injury accompanied by hemorrhagic shock triggers an early release of cell constituents into the circulation, referred to as the systemic storm. The systemic storm drives the systemic inflammatory response and is associated with increased morta...
Development and validation of a multi-omics hemorrhagic transformation model based on hyperattenuated imaging markers following mechanical thrombectomy.
Scientific reports
May 23, 2025
This study aimed to develop a predictive model integrating clinical, radiomics, and deep learning (DL) features of hyperattenuated imaging markers (HIM) from computed tomography scans immediately following mechanical thrombectomy (MT) to predict hemo...
Association between fibrinogen levels and prognosis in critically bleeding patients: exploration of the optimal therapeutic threshold.
European journal of trauma and emergency surgery : official publication of the European Trauma Society
May 23, 2025
BACKGROUND: Severe bleeding is a leading cause of ICU admission and mortality. Fibrinogen plays a crucial role in prognosis, yet optimal thresholds and supplementation targets remain unclear.
Machine learning-based prediction of bleeding risk in extracorporeal membrane oxygenation patients using transfusion as a surrogate marker.
Transfusion
Apr 25, 2025
BACKGROUND: The increasing use of extracorporeal membrane oxygenation (ECMO) has highlighted challenges in managing bleeding complications. Optimal transfusion strategies remain uncertain for this diverse patient group, necessitating accurate predict...
Prediction of moderate to severe bleeding risk in pediatric immune thrombocytopenia using machine learning.
European journal of pediatrics
Apr 7, 2025
UNLABELLED: This study aimed to develop and validate a risk prediction model for moderate to severe bleeding in children with immune thrombocytopenia (ITP). Data from 286 ITP patients were prospectively collected and randomly split into training (80%...
Natural language processing for identifying major bleeding risk in hospitalised medical patients.
Computers in biology and medicine
Mar 30, 2025
BACKGROUND: Major bleeding is a severe complication in critically ill medical patients, resulting in significant morbidity, mortality, and healthcare costs. This study aims to assess the incidence and risk factors for major bleeding in hospitalised m...
Machine Learning Predicts Bleeding Risk in Atrial Fibrillation Patients on Direct Oral Anticoagulant.
The American journal of cardiology
Feb 25, 2025
Predicting major bleeding in nonvalvular atrial fibrillation (AF) patients on direct oral anticoagulants (DOACs) is crucial for personalized care. Alternatives like left atrial appendage closure devices lower stroke risk with fewer nonprocedural blee...
Accurate prediction of bleeding risk after coronary artery bypass grafting with dual antiplatelet therapy: A machine learning model vs. the PRECISE-DAPT score.
International journal of cardiology
Dec 22, 2024
BACKGROUND: Dual antiplatelet therapy (DAPT) after coronary artery bypass grafting (CABG), although might be protective for ischemic events, can lead to varying degrees of bleeding, resulting in serious clinical events, including death. This study ai...
Development of a simplified model and nomogram for the prediction of pulmonary hemorrhage in respiratory distress syndrome in extremely preterm infants.
BMC pediatrics
Nov 21, 2024
BACKGROUND: Pulmonary hemorrhage (PH) in respiratory distress syndrome (RDS) in extremely preterm infants exhibits a high mortality rate and poor long-term outcomes. The aim of the present study was to develop a machine learning (ML) predictive model...
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