Predicting Estimated Blood Loss and Transfusions in Gynecologic Surgery Using Artificial Neural Networks

Steven Walczak and Emad Mikhail
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Steven Walczak: University of South Florida, USA
Emad Mikhail: University of South Florida, USA

International Journal of Healthcare Information Systems and Informatics (IJHISI), 2021, vol. 16, issue 1, 1-15

Abstract: This chapter explores valuating the efficacy of using artificial neural networks (ANNs) for predicting the estimated blood loss (EBL) and also transfusion requirements of myomectomy patients. All 146 myomectomy surgeries performed over a 6-year period from a single site are captured. Records were removed for various reasons, leaving 96 cases. Backpropagation and radial basis function ANN models were developed to predict EBL and perioperative transfusion needs along with a regression model. The single hidden layer backpropagation ANN models performed the best for both prediction problems. EBL was predicted on average within 127.33 ml of measured blood loss, and transfusions were predicted with 71.4% sensitivity and 85.4% specificity. A combined ANN ensemble model using the output of the EBL ANN as an input variable to the transfusion prediction ANN was developed and resulted in 100% sensitivity and 62.9% specificity. The preoperative identification of large EBL or transfusion need can assist caregivers in better planning for possible post-operative morbidity and mortality.

Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:igg:jhisi0:v:16:y:2021:i:1:p:1-15

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