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Chaos based blood glucose prediction and insulin adjustment for diabetes mellitus

机译:基于混沌的血糖预测和胰岛素调节

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Many diabetes mellitus (DM) patients are concerned about unstable blood glucose, even with regular monitoring by a family doctor. The insulin preparation shows peak action at several hours after subcutaneous administration. For this reason, unstable blood glucose is often caused by intensive insulin therapy with self-monitored blood glucose (SMBG). The insulin requirement is determined in proportion to fasting blood glucose (FBG) in the sliding scale method. A fixed amount of insulin is administered regardless of FBG level in the constant insulin method. It is indispensable to estimate FBG at peak time, when insulin works the hardest, to obtain the appropriate effect of insulin administration. We employed the local fuzzy reconstruction method based on chaos theory for predicting FBG at peak time. The direction to change the FBG at peak time is predicted with a 70-90% success rate. The amount of insulin administration is adjusted based on predicted FBG level. After predictive glycemic control (PGC) for around one year, the FBG average approached the normal range, standard deviation (SD) reduced by half, and hyperglycemia decreased.
机译:许多糖尿病(DM)患者都担心血糖不稳定,即使由家庭医生定期监控也是如此。胰岛素制剂在皮下给药后数小时显示出峰值作用。因此,不稳定的血糖通常是由自我监测血糖(SMBG)的强化胰岛素治疗引起的。在滑动标度法中,胰岛素需要量与空腹血糖(FBG)成正比。在恒定胰岛素方法中,不管FBG水平如何,都施用固定量的胰岛素。当胰岛素工作最困难时,估计峰值时间的FBG必不可少,以获得适当的胰岛素给药效果。我们采用基于混沌理论的局部模糊重建方法来预测峰值时间的FBG。预测在高峰时间更改FBG的方向具有70-90%的成功率。根据预测的FBG水平调整胰岛素的给药量。在进行了预测性血糖控制(PGC)约一年后,FBG平均值接近正常范围,标准差(SD)降低了一半,高血糖症降低了。

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