Two-stage hemoglobin prediction based on prior causality

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 non-invasive prediction for patients' Hb content is of enormous significance.

In this study, targeted toward the palpebral conjunctiva images in perioperative patients, a non-invasive 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 0.512, the explained variance score can reach 0.535, and the mean absolute error is 1.521.

In this study, we proposed to predict the accurate hemoglobin concentration and finally constructed a model using the deep learning method to predict eyelid Hb of perioperative patients based on the a priori casual knowledge.