AIMC Topic: Blood Transfusion
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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...
O blood usage trends in the pediatric population 2015-2019: A multi-institutional analysis.
Transfusion
Mar 28, 2025
BACKGROUND: In 2019, AABB released the bulletin "Recommendations on the Use of Group O Red Blood Cells" in which the recommendations about pediatric and neonatal blood transfusions were limited. Eight U.S. pediatric hospitals sought to determine tren...
Predicting blood transfusion demand in intensive care patients after surgery by comparative analysis of temporally extended data selection.
BMC medical informatics and decision making
Dec 18, 2024
BACKGROUND: Blood transfusion (BT) is a critical aspect of medical care for surgical patients in the Intensive Care Unit (ICU). Timely and accurate identification of BT needs can enhance patient outcomes and healthcare resource management.
Privacy-preserving federated data access and federated learning: Improved data sharing and AI model development in transfusion medicine.
Transfusion
Nov 29, 2024
BACKGROUND: Health data comprise data from different aspects of healthcare including administrative, digital health, and research-oriented data. Together, health data contribute to and inform healthcare operations, patient care, and research. Integra...
Assessment of machine learning classifiers for predicting intraoperative blood transfusion in non-cardiac surgery.
Transfusion clinique et biologique : journal de la Societe francaise de transfusion sanguine
Oct 18, 2024
BACKGROUND: This study aimed to develop a machine learning classifier for predicting intraoperative blood transfusion in non-cardiac surgeries.
A machine learning-based Coagulation Risk Index predicts acute traumatic coagulopathy in bleeding trauma patients.
The journal of trauma and acute care surgery
Sep 27, 2024
BACKGROUND: Acute traumatic coagulopathy (ATC) is a well-described phenomenon known to begin shortly after injury. This has profound implications for resuscitation from hemorrhagic shock, as ATC is associated with increased risk for massive transfusi...
The practical use of artificial intelligence in Transfusion Medicine and Apheresis.
Transfusion and apheresis science : official journal of the World Apheresis Association : official journal of the European Society for Haemapheresis
Sep 10, 2024
BACKGROUND: Blood and plasma volume calculations are a daily part of practice for many Transfusion Medicine and Apheresis practitioners. Though many formulas exist, each facility may have their own modifications to consider. ChatGPT (Generative Pre-t...
Prediction of transfusion risk after total knee arthroplasty: use of a machine learning algorithm.
Orthopaedics & traumatology, surgery & research : OTSR
Sep 3, 2024
INTRODUCTION: Total knee arthroplasty (TKA) carries a significant hemorrhagic risk, with a non-negligible rate of postoperative transfusions. The blood-sparing strategy has evolved to reduce blood loss after TKA by identifying the patient's risk fact...
Evaluation of the Factors Influencing Blood Transfusion during Minimally Invasive Direct Coronary Artery Bypass Surgery.
Cardiology
Jul 26, 2024
INTRODUCTION: The objective of this study was to analyze the blood transfusion factors of minimally invasive direct coronary artery bypass (MIDCAB) surgery using artificial intelligence.
Machine learning models predict triage levels, massive transfusion protocol activation, and mortality in trauma utilizing patients hemodynamics on admission.
Computers in biology and medicine
Jul 16, 2024
BACKGROUND: The effective management of trauma patients necessitates efficient triaging, timely activation of Massive Blood Transfusion Protocols (MTP), and accurate prediction of in-hospital outcomes. Machine learning (ML) algorithms have emerged as...
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