Predicting the Need for Blood Transfusions in Cardiac Surgery: A Comparison between Machine Learning Algorithms and Established Risk Scores in the Brazilian Population.
Cristiano Berardo Carneiro da CunhaTiago Andrade LimaDiogo Luiz de Magalhães FerrazIgor Tiago Correia SilvaMatheus Kennedy Dionisio SantiagoGabrielle Ribeiro SenaVerônica Soares MonteiroLívia Barbosa AndradePublished in: Brazilian journal of cardiovascular surgery (2024)
The findings of this study suggest that ML algorithms may offer a more accurate prediction of the need for blood transfusions than the traditional scoring systems and could enhance the accuracy of predicting blood transfusion requirements in cardiac surgery patients. Further research could focus on optimizing and refining ML algorithms to improve their accuracy and make them more suitable for clinical use.