Ensemble learning approaches to predicting complications of blood transfusion. Conference Paper uri icon

Overview

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.

publication date

  • August 1, 2015

Research

keywords

  • Acute Lung Injury
  • Forecasting
  • Machine Learning
  • Transfusion Reaction

Identity

PubMed Central ID

  • PMC4844027

Scopus Document Identifier

  • 84953318257

Digital Object Identifier (DOI)

  • 10.1109/EMBC.2015.7320058

PubMed ID

  • 26737958

Additional Document Info

volume

  • 2015