TY - GEN
T1 - Ensemble learning approaches to predicting complications of blood transfusion
AU - Murphree, Dennis
AU - Ngufor, Che
AU - Upadhyaya, Sudhindra
AU - Madde, Nagesh
AU - Clifford, Leanne
AU - Kor, Daryl J.
AU - Pathak, Jyotishman
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/11/4
Y1 - 2015/11/4
N2 - Of the 21 million blood components transfused in the United States during 2011, approximately 1 in 414 resulted in complication [1]. Two complications in particular, transfusion-related acute lung injury (TRALI) and transfusion-associated circulatory overload (TACO), are especially concerning. These two alone accounted for 62% of reported transfusion-related fatalities in 2013 [2]. 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.
AB - Of the 21 million blood components transfused in the United States during 2011, approximately 1 in 414 resulted in complication [1]. Two complications in particular, transfusion-related acute lung injury (TRALI) and transfusion-associated circulatory overload (TACO), are especially concerning. These two alone accounted for 62% of reported transfusion-related fatalities in 2013 [2]. 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.
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U2 - 10.1109/EMBC.2015.7320058
DO - 10.1109/EMBC.2015.7320058
M3 - Conference contribution
C2 - 26737958
AN - SCOPUS:84953318257
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 7222
EP - 7225
BT - 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015
Y2 - 25 August 2015 through 29 August 2015
ER -