Predicting Adverse Reactions to Blood Transfusion

Murphree, Dennis H.; Clifford, Leanne; Lin, Yaxiong; Madde, Nagesh; Ngufor, Che; Upadhyaya, Sudhindra; Pathak, Jyotishman; Kor, Daryl J.

doi:10.1109/ichi.2015.17

Cited by 4 publications

(3 citation statements)

References 16 publications

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“…For the retrospective chart review we will follow a procedure similar to that used in [3][4][5]. Using data from the Perioperative Datamart, a natural language processing (NLP) enhanced version of a surveillance algorithm [13] will identify patients who are likely to have suffered transfusionrelated complications.…”

Section: Future Work and Conclusionmentioning

confidence: 99%

“…Recognizing the importance of early identification of patients at high risk for TACO or TRALI, our group has developed models [5] for predicting the likelihood of these adverse reactions. Our models include both traditional logistic regression as well as modern machine learning techniques, and incorporate oversampling methods to deal with imbalance in the distribution of patient groups who experienced adverse reactions and those who did not.…”

Section: Introductionmentioning

confidence: 99%

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A Clinical Decision Support System for Preventing Adverse Reactions to Blood Transfusion

Murphree

Clifford

Lin

et al. 2015

2015 International Conference on Healthcare Informatics

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During 2011 approximately 21 million blood components were transfused in the United States, with roughly 1 in 414 resulting in complication. For Americans, the two leading causes of transfusion-related death are the respiratory complications Transfusion-related acute lung injury (TRALI) and Transfusion-associated circulatory overload (TACO). Each of these complications results in significantly longer ICU and hospital stays as well as significantly greater rates of mortality. We have developed a set of machine learning models for predicting the likelihood of these adverse reactions in surgical populations. Here we describe deploying these models into a perioperative critical care environment via a continuous monitoring and alerting clinical decision support system. The goal of this system, which directly integrates our suite of machine learning models running in the R statistical environment into a traditional health information system, is to improve transfusion-related outcomes in the perioperative environment. By identifying high-risk patients prior to transfusion, the clinical team may be able to choose a more appropriate therapy or therapeutic course. Identifying highrisk patients for increased observation after transfusion may also allow for a more timely intervention, thereby potentially improving care delivery and resulting patient outcome. An early prototype of this system is currently running in two Mayo Clinic perioperative environments.

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Section: Future Work and Conclusionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Clinical Decision Support System for Preventing Adverse Reactions to Blood Transfusion

Murphree

Clifford

Lin

et al. 2015

2015 International Conference on Healthcare Informatics

Self Cite

View full text Add to dashboard Cite

show abstract

“…In a previous work [6], we constructed several learning models, hereafter called base models, built on a dataset collected by Clifford et al [4, 5]. One particularly appealing aspect of this dataset is the physician-adjudicated “ground truth” for patients suffering TACO/TRALI.…”

Section: Introductionmentioning

confidence: 99%

Ensemble learning approaches to predicting complications of blood transfusion

Murphree

Ngufor

Upadhyaya

et al. 2015

2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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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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