肾脏疾病
维生素D与神经学
甲状旁腺激素
医学
加药
重症监护医学
疾病
计算机科学
生物信息学
内科学
钙
生物
作者
Adam E. Gaweda,Eleanor D. Lederer,Michael E. Brier
摘要
Chronic kidney disease (CKD)-mineral bone disorder (MBD) is a complex clinical syndrome that begins early during CKD and evolves into one of the deadliest complications of CKD through its effects on the cardiovascular and skeletal systems. Achievement of treatment goals to decrease the risk of accelerated cardiovascular events and fractures has been challenging. We hypothesized that application of quantitative systems pharmacology (QSP) modeling combined with artificial intelligence techniques could improve the management of CKD-MBD with the goal of improving outcomes for patients with CKD. We present the implementation of a reinforcement learning (RL) approach to achieve the prescribed goals for serum calcium, phosphorus, and parathyroid hormone through concurrent dosing of phosphate binders, vitamin D analogs, and calcimimetics by simulation in 80 subjects in Matlab. In silico simulation results demonstrate that the application of a QSP model coupled with RL more effectively and quickly achieves treatment goals even in the setting of inferior simulated subject compliance with medical therapy and identifies key decision variables for therapeutic recommendations.
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