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

Source Title: Research Anthology on Advancements in Women's Health and Reproductive Rights

ISBN13: 9781668462997|ISBN10: 1668462990|EISBN13: 9781668463000

DOI: 10.4018/978-1-6684-6299-7.ch003

MLA

Walczak, Steven, and Emad Mikhail. "Predicting Estimated Blood Loss and Transfusions in Gynecologic Surgery Using Artificial Neural Networks." Research Anthology on Advancements in Women's Health and Reproductive Rights, edited by Information Resources Management Association, IGI Global, 2022, pp. 30-46. https://doi.org/10.4018/978-1-6684-6299-7.ch003

APA

Walczak, S. & Mikhail, E. (2022). Predicting Estimated Blood Loss and Transfusions in Gynecologic Surgery Using Artificial Neural Networks. In I. Management Association (Ed.), Research Anthology on Advancements in Women's Health and Reproductive Rights (pp. 30-46). IGI Global. https://doi.org/10.4018/978-1-6684-6299-7.ch003

Chicago

Walczak, Steven, and Emad Mikhail. "Predicting Estimated Blood Loss and Transfusions in Gynecologic Surgery Using Artificial Neural Networks." In Research Anthology on Advancements in Women's Health and Reproductive Rights, edited by Information Resources Management Association, 30-46. Hershey, PA: IGI Global, 2022. https://doi.org/10.4018/978-1-6684-6299-7.ch003

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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.