Disclosed in the present application are an HCT prediction method and
system, a device, and a medium. Photons in a
photon group are emitted into an
extracorporeal circulation blood model so that the photons are reflected, scattered, or absorbed during movement. The
extracorporeal circulation blood model is a model constructed on the basis of the thickness of an
extracorporeal circulation tube, the thickness of the blood, and the distance from a
light source generator to a
detector.
Light intensity data corresponding to photons reaching the
detector are acquired. The acquired
light intensity data are input into a prediction model to obtain a
hematocrit (HCT) prediction value. The prediction model is a model constructed on the basis of a BP neural network and the relationship between
light intensity and HCT. The prediction model combining
simulation and neural network increases the data volume, achieves continuous non-invasive
estimation of HCT by means of
algorithm model optimization, and improves the accuracy of HCT prediction.