2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) 2015
DOI: 10.1109/embc.2015.7320058
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Ensemble learning approaches to predicting complications of blood transfusion

Abstract: 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 … Show more

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Cited by 12 publications
(14 citation statements)
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“…The standard classifiers for the heterogeneous ensemble considered in this study are described as follows: Bayes Theory (Naïve Bayes algorithm), Instance Learning (k Nearest Neighbour), Rule-based (RIPPER) and Tree methods (C4.5 Decision Tree). The voting combination method [14] [15] was adopted in this study for building the heterogeneous ensemble method. The voting method is a noncomplicated method of combining several predictions of varied or different models, and it can be implemented in a variety of approaches, including majority vote, minority vote and average of probabilities.…”
Section: A Datasetmentioning
confidence: 99%
“…The standard classifiers for the heterogeneous ensemble considered in this study are described as follows: Bayes Theory (Naïve Bayes algorithm), Instance Learning (k Nearest Neighbour), Rule-based (RIPPER) and Tree methods (C4.5 Decision Tree). The voting combination method [14] [15] was adopted in this study for building the heterogeneous ensemble method. The voting method is a noncomplicated method of combining several predictions of varied or different models, and it can be implemented in a variety of approaches, including majority vote, minority vote and average of probabilities.…”
Section: A Datasetmentioning
confidence: 99%
“…Murphree et al applied a large number of different model approaches to a related topic, i.e. the prediction of complications after blood transfusion [11]. Their results indicate that most models give good results if applied alone and that combining those models with a "majority vote" strategy did not yield a significant improvement.…”
Section: Predictive Modelling For Blood Transfusion Predictionmentioning
confidence: 99%
“…The algorithms may be used by other investigators and policy-makers to estimate the extent of misclassification, and in formal bias analyses to adjust effect estimates [ 23 ]. Alternatively, given that none of the algorithms demonstrated exemplary accuracy, integrating multiple algorithms using methods such as majority vote and Boolean operations may be another way these algorithms may be implemented in practice [ 24 ].…”
Section: Discussionmentioning
confidence: 99%