| 引用本文: | 赵婷,李红健,章立华,冯杰,王婷婷,孙力,于鲁海.基于人工神经网络模型的新疆维吾尔族癫痫患儿左乙拉西坦血清药物浓度预测研究[J].中国现代应用药学,2021,38(22):2875-2880. |
| ZHAO Ting,LI Hongjian,ZHANG Lihua,FENG Jie,WANG Tingting,SUN Li,YU Luhai.Prediction Study of Serum Concentration of Levetiracetam in Children with Epilepsy of Uygur Nationality in Xinjiang Based on Artificial Neural Network Model[J].Chin J Mod Appl Pharm(中国现代应用药学),2021,38(22):2875-2880. |
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| 摘要: |
| 目的 建立新疆维吾尔族癫痫患儿左乙拉西坦（levetiracetam，LEV）稳态血清药物浓度的人工神经网络预测模型，为其临床个体化给药提供参考。方法 测定330例新疆维吾尔族癫痫患儿LEV稳态血清药物浓度，收集临床资料，采用人工神经网络构建LEV血清药物浓度预测模型。结果 模型验证结果显示，50例维吾尔族癫痫患儿LEV血清药物浓度中，平均预测误差为（–2.15±6.97）%（<5%），预测误差<±20%的比率为96.00%（47/50），均方误差为（1.11±2.23）%（<5%），均方预测误差为（52.16±106.81）%（<100%），均方根预测误差为（5.27±4.99）%（<10%）。维吾尔族癫痫患儿口服LEV后血清药物浓度预测值和实测值之间相关性较高（r=0.986 1）。这些预测结果表明，该模型具有较好的预测性能，可以用于LEV血清药物浓度的预测。结论 应用人工神经网络预测LEV血清药物浓度是可行的，可将其用于LEV个体化给药的研究，促进临床合理用药。 |
| 关键词: 人工神经网络 左乙拉西坦 血清药物浓度 |
| DOI：10.13748/j.cnki.issn1007-7693.2021.22.020 |
| 分类号:R969.1 |
| 基金项目:新疆维吾尔自治区自然科学基金资助项目（2016D01C097） |
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| Prediction Study of Serum Concentration of Levetiracetam in Children with Epilepsy of Uygur Nationality in Xinjiang Based on Artificial Neural Network Model |
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ZHAO Ting1, LI Hongjian1, ZHANG Lihua2, FENG Jie1, WANG Tingting1, SUN Li1, YU Luhai1
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1.Department of Pharmacy, People's Hospital of Xinjiang Uygur Autonomous Region, Urumqi 830000, China;2.Postdoctoral Mobile Station, Xiamen University, Xiamen 361000, China
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| Abstract: |
| OBJECTIVE To establish artificial neural network model for predicting the steady-state serum concentration of levetiracetam(LEV) in Uygur children with epilepsy in Xinjiang, so as to provide reference for clinical individualized drug administration. METHODS The steady-state serum drug concentration of levetiracetam in 330 cases Uyghur children with epilepsy in Xinjiang was determined, the clinical data was collected, and the artificial neural network was used to construct the prediction model of LEV serum concentration. RESULTS The results of model verification showed that the mean prediction error was (-2.15±6.97)%(<5%), the ratio of prediction error <±20% was 96.00%(47/50), mean absolute prediction error was (1.11±2.23)%(<5%), mean square prediction error was (52.16±106.81)%(<100%), and root mean square prediction error was (5.27±4.99)%(<10%) in the serum concentration of LEV in 50 children with epilepsy of Uygur nationality. There was a high correlation between the predicted and measured values of serum concentration after oral administration of LEV in children with epilepsy of Uygur nationality(r=0.986 1). These results showed that the model had good prediction performance and could be used to predict the serum concentration of LEV. CONCLUSION It is feasible to predict the serum concentration of LEV by using artificial neural network, and it can be used in the study of individual drug administration of levetiracetam to promote the rational use of drugs in clinic. |
| Key words: artificial neural network levetiracetam serum concentration |
