Sproule B A, Bazoon M, Shulman K I, Turksen I B, Naranjo C A
Psychopharmacology Research Program, Sunnybrook Health Science Centre, Toronto, Ontario, Canada.
Clin Pharmacol Ther. 1997 Jul;62(1):29-40. doi: 10.1016/S0009-9236(97)90149-1.
We hypothesized that fuzzy logic could be used for pharmacokinetic modeling. Our objectives were to develop and evaluate a model for predicting serum lithium concentrations with fuzzy logic.
Steady-state pharmacokinetic data had been previously collected in 10 elderly patients (age range, 67 to 80 years) with depression who were receiving lithium once daily. Each patient had serial serum lithium concentration determinations over one 24-hour period. The resulting 137 data sets initially consisted of five input variables (age, weight, serum creatinine, lithium dose, and time since last dose) and one output variable (serum lithium concentration; range, 0.2 to 1.24 mmol/L).
A fuzzy rulebase was created with 87 randomly chosen data sets, and predictions of serum lithium concentration were made on the basis of the remaining 50 data sets. All of the input variables except age and weight were identified as contributing to the fuzzy logic model. The average magnitude of the error in the predictions was 0.13 mmol/L (root mean squared error) with a bias (mean of the prediction errors) of 0.03 mmol/L.
This study indicates that the use of fuzzy logic for pharmacokinetic modeling of lithium for serum concentration predictions is feasible.
我们假设模糊逻辑可用于药代动力学建模。我们的目标是开发并评估一个使用模糊逻辑预测血清锂浓度的模型。
此前已收集了10名患有抑郁症的老年患者（年龄范围为67至80岁）的稳态药代动力学数据，这些患者每天服用一次锂盐。每位患者在一个24小时时间段内进行了系列血清锂浓度测定。得到的137个数据集最初包含五个输入变量（年龄、体重、血清肌酐、锂剂量以及距上次给药的时间）和一个输出变量（血清锂浓度；范围为0.2至1.24 mmol/L）。
用87个随机选择的数据集创建了一个模糊规则库，并基于其余50个数据集对血清锂浓度进行了预测。除年龄和体重外，所有输入变量均被确定对模糊逻辑模型有贡献。预测误差的平均幅度为0.13 mmol/L（均方根误差），偏差（预测误差的均值）为0.03 mmol/L。
本研究表明，使用模糊逻辑对锂进行药代动力学建模以预测血清浓度是可行的。
