. We have previously developed a set of machine learning base models for predicting the likelihood of these adverse reactions, with a goal towards better informing the clinician prior to a transfusion decision. Here we describe recent work incorporating ensemble learning approaches to predicting TACO/TRALI. In particular we describe combining base models via majority voting, stacking of model sets with varying diversity, as well as a resampling/boosting combination algorithm called RUSBoost. We find that while the performance of many models is very good, the ensemble models do not yield significantly better performance in terms of AUC.
In REF , The authors presented integrating base models via stacking of model ,majority voting sets with varying diversity, as well as a re sampling/boosting integration technique called RUSBoost to predicting disorders that occurs in blood transfusion.
Ensemble learning approaches to predicting complications of blood transfusion
2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Computer Science Medicine