Non-invasive in vitro HCT estimation by Monte Carlo modeling and simulation

Continuous monitoring of Hematocrit (HCT) is essential for the treatment of patients during cardiopulmonary bypass. Noninvasive monitoring is helpful to prevent the formation of thrombosis and reduce the risk of treatment. However, the problem of insufficient sampling data further makes it difficult to improve the accuracy of existing prediction methods. Aiming at the problem of sampling difficulty, a data simulation model was proposed. The data simulation model is based on Monte Carlo (MC) photon simulation algorithm. Then, the light intensity data after passing through the blood obtained. Aiming at the problem of low accuracy of existing prediction models, two prediction models are further proposed to estimate HCT: backward iterative interpolation estimation model and BP neural network prediction model, and then the estimated values were compared with the actual values. The experimental results show that the MC data simulation model can reflect the real data change trend. When the simulated data are applied to the reverse interpolation estimation model and the BP neural network prediction model, the root mean square error of the former is 1.39%, and the latter is 0.61%. Bland-Altman analysis was also performed to confirm the accuracy.