Ensemble learning approaches to predicting complications of blood transfusion

Dennis Murphree, Che Ngufor, Sudhindra Upadhyaya, Nagesh Madde, Leanne Clifford, Daryl J Kor, Jyotishman Pathak

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Scopus citations

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 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.

Original languageEnglish (US)
Title of host publicationProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages7222-7225
Number of pages4
Volume2015-November
ISBN (Print)9781424492718
DOIs
StatePublished - Nov 4 2015
Event37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015 - Milan, Italy
Duration: Aug 25 2015Aug 29 2015

Other

Other37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015
CountryItaly
CityMilan
Period8/25/158/29/15

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Biomedical Engineering
  • Health Informatics

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  • Cite this

    Murphree, D., Ngufor, C., Upadhyaya, S., Madde, N., Clifford, L., Kor, D. J., & Pathak, J. (2015). Ensemble learning approaches to predicting complications of blood transfusion. In Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS (Vol. 2015-November, pp. 7222-7225). [7320058] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EMBC.2015.7320058