AUTHOR=Chen Yuwen , Zhong Kunhua , Zhu Yiziting , Sun Qilong 

TITLE=Two-stage hemoglobin prediction based on prior causality

JOURNAL=Frontiers in Public Health

VOLUME=Volume 10 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2022.1079389

DOI=10.3389/fpubh.2022.1079389

ISSN=2296-2565

ABSTRACT=Perioperative hemoglobin (Hb) levels can influence tissue metabolism. For clinical physicians, precise Hb concentration greatly contributes to intraoperative blood transfusion. The reduction in Hb during an operation weakens blood’s oxygen-carrying capacity and poses threats to multiple systems and organs of the whole body. Patients can die from perioperative anemia. Thus, a timely and accurate noninvasive prediction for patients’ Hb content is of enormous significance. In this study, targeted toward the palpebral conjunctiva images in perioperative patients, a noninvasive model for predicting Hb levels is constructed by means of deep neural semantic segmentation and a convolutional network based on a priori causal knowledge, then an automatic framework was proposed to predict the precise concentration value of Hb. Specifically, according to a priori causal knowledge, the palpebral region was positioned first, and patients' Hb concentration was subjected to regression prediction using a neural network. The model proposed in this study was experimented on using actual medical datasets, the R^2 of the model proposed can reach 0.502, the explained variance score can reach 0.518, and the mean absolute error is 1.617, revealing its effectiveness and practicality.