| 引用本文: | 吴青,李卓,沈一飞.基于深度Q网络的BECT治疗药物左乙拉西坦用药剂量推荐[J].中国现代应用药学,2022,39(12):1585-1590. |
| WU Qing,LI Zhuo,SHEN Yifei.Dosage Recommendation of Levetiracetam for the Treatment of BECT in Children Based on Deep Q Network[J].Chin J Mod Appl Pharm(中国现代应用药学),2022,39(12):1585-1590. |
|
| 本文已被：浏览 982次 下载 628次 |
码上扫一扫！ |
|
|
| 基于深度Q网络的BECT治疗药物左乙拉西坦用药剂量推荐 |
|
吴青1, 李卓2, 沈一飞3
|
|
1.重庆医科大学附属儿童医院药剂部, 国家儿童健康与疾病临床医学研究中心, 儿童发育疾病研究教育部重点实验室, 儿科学重庆市重点实验室, 重庆 400014;2.重庆邮电大学计算机科学与技术学院, 重庆 400065;3.香港理工大学电子计算系, 中国香港 999077
|
|
| 摘要: |
| 目的 基于深度Q网络模型推荐伴中央颞区棘波的儿童良性癫痫患儿抗癫痫发作治疗药物左乙拉西坦的口服剂量，辅助医师制定精准的个性化用药方案。方法 收集整理2016年1月1日—2021年4月29日重庆医科大学附属儿童医院245例伴中央颞区棘波的儿童良性癫痫患儿的随访数据，利用深度强化学习技术，构建一个基于深度Q网络的儿童癫痫智能用药剂量推荐模型，并将专业医师处方与算法推荐的左乙拉西坦每日用药总剂量进行比较。结果 在推荐每日用药总剂量分布上，深度Q网络推荐的分布情况跟专业医师处方大体相似且更倾向于推荐在数据集当中分布较多并且具有统计意义的用药剂量。对基于深度Q网络剂量推荐在伴中央颞区棘波的儿童良性癫痫治疗药物左乙拉西坦的用药剂量的准确性进行了比较，每日用药总剂量分类平均准确率为89.7%，分类推荐用药平均误差为0.341 3。结论 初步验证了基于深度Q网络的用药剂量推荐模型的有效性，为该算法模型推广到更多抗癫痫发作治疗药物剂量推荐中提供参考。 |
| 关键词: 伴中央颞区棘波的儿童良性癫痫 左乙拉西坦 剂量推荐 深度强化学习 深度Q网络 |
| DOI：10.13748/j.cnki.issn1007-7693.2022.12.012 |
| 分类号:R969.3 |
| 基金项目:重庆市科卫联合医学科研项目(2021MSXM234);儿童医疗保障创新研究项目(NCRCCHD-2019-HP-15) |
|
| Dosage Recommendation of Levetiracetam for the Treatment of BECT in Children Based on Deep Q Network |
|
WU Qing1, LI Zhuo2, SHEN Yifei3
|
|
1.Department of Pharmacy, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, Chongqing 400014, China;2.School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China;3.Department of Computing, The University of Hong Kong Polytechnic University, Hong Kong 999077, China
|
| Abstract: |
| OBJECTIVE To recomend the oral dose of levetiracetam for the treatment of benign childhood epilepsy with centro-temporal spikes and assist doctors in making precise individual therapy based on deep Q network. METHODS The follow-up clinical data of 245 cases of benign childhood epilepsy with centro-temporal spiked from Children's Hospital of Chongqing Medical University from 2016 January 1 to 2021 April 29. Leveraging deep reinforcement learning technology, a deep Q neural network model for drug dosage recommendations of childhood epilepsy was build. The professional physician's prescription was compared with the total daily dosage of levetiracetam recommended by the algorithm. RESULTS In terms of the total recommended daily dose distribution, the distribution recommended by the deep Q network was generally similar to that of professional physicians, and it was more inclined to recommend drug doses that were widely distributed and statistically significant in the data set. The accuracy of the intelligent dose recommendation of levetiracetam for benign epilepsy in children with centro-temporal spikes was compared in the deep Q network dose recommendation. The average accuracy rate of the total dose classification of daily medication was 89.7%, and the average error of the classification of recommended drugs was 0.341 3. CONCLUSION The effectiveness of the drug dose recommendation model based on the deep Q network is preliminarily verified, and it provides a reference for the extension of the algorithm model to more anti-epileptic seizure drug dose recommendations. |
| Key words: benign childhood epilepsy with centro-temporal spikes levetiracetam drug dosage recommendation deep reinforcement learning deep Q network |
