Bellazzi R
Dipartimento di Informatica e Sistemistica, Universitá di Pavia, Italy.
Comput Biomed Res. 1993 Jun;26(3):274-93. doi: 10.1006/cbmr.1993.1019.
This paper describes how Bayesian networks can be used in combination with compartmental models to plan recombinant human erythropoietin delivery in the treatment of anemia of chronic uremic patients. Past measurements of hemoglobin concentration in a patient during the therapy can be exploited to adjust the parameters of a compartmental model of erythropoiesis. This adaptive process provides more accurate patient-specific predictions, and hence a more rational dosage planning. Inferences are performed by using a stochastic simulation algorithm called Gibbs sampling. We describe a drug delivery optimization protocol based on our approach. Some results obtained on real data are presented.
本文描述了如何将贝叶斯网络与房室模型相结合，以规划重组人促红细胞生成素的给药方案，用于治疗慢性尿毒症患者的贫血。在治疗过程中对患者血红蛋白浓度的既往测量结果可用于调整红细胞生成房室模型的参数。这种自适应过程可提供更准确的患者特异性预测，从而实现更合理的剂量规划。推理是通过使用一种称为吉布斯采样的随机模拟算法进行的。我们基于我们的方法描述了一种药物递送优化方案。展示了在真实数据上获得的一些结果。
