Welcome to the InfoSci Platform
Reference Hub1
Predicting Estimated Blood Loss and Transfusions in Gynecologic Surgery Using Artificial Neural Networks

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

Steven Walczak, Emad Mikhail
Copyright: © 2021 |Volume: 16 |Issue: 1 |Pages: 15
ISSN: 1555-3396|EISSN: 1555-340X|EISBN13: 9781799859789|DOI: 10.4018/IJHISI.2021010101
Cite Article Cite Article

MLA

Walczak, Steven, and Emad Mikhail. "Predicting Estimated Blood Loss and Transfusions in Gynecologic Surgery Using Artificial Neural Networks." IJHISI vol.16, no.1 2021: pp.1-15. http://doi.org/10.4018/IJHISI.2021010101

APA

Walczak, S. & Mikhail, E. (2021). Predicting Estimated Blood Loss and Transfusions in Gynecologic Surgery Using Artificial Neural Networks. International Journal of Healthcare Information Systems and Informatics (IJHISI), 16(1), 1-15. http://doi.org/10.4018/IJHISI.2021010101

Chicago

Walczak, Steven, and Emad Mikhail. "Predicting Estimated Blood Loss and Transfusions in Gynecologic Surgery Using Artificial Neural Networks," International Journal of Healthcare Information Systems and Informatics (IJHISI) 16, no.1: 1-15. http://doi.org/10.4018/IJHISI.2021010101

Export Reference

Mendeley
Favorite Full-Issue Download

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.